MR spectroscopy system and method for diagnosing painful and non-painful  intervertebral discs

ABSTRACT

An MR Spectroscopy (MRS) system and approach is provided for diagnosing painful and non-painful discs in chronic, severe low back pain patients (DDD-MRS). A DDD-MRS pulse sequence generates and acquires DDD-MRS spectra within intervertebral disc nuclei for later signal processing &amp; diagnostic analysis. An interfacing DDD-MRS signal processor receives output signals of the DDD-MRS spectra acquired and is configured to optimize signal-to-noise ratio (SNR) by an automated system that selectively conducts optimal channel selection, phase and frequency correction, and frame editing as appropriate for a given acquisition series. A diagnostic processor calculates a diagnostic value for the disc based upon a weighted factor set of criteria that uses MRS data extracted from the acquired and processed MRS spectra along regions associated with multiple chemicals that have been correlated to painful vs. non-painful discs. A diagnostic display provides a scaled, color coded legend and indication of results for each disc analyzed as an overlay onto a mid-sagittal T2-weighted MRI image of the lumbar spine for the patient being diagnosed. Clinical application of the embodiments provides a non-invasive, objective, pain-free, reliable approach for diagnosing painful vs. non-painful discs by simply extending and enhancing the utility of otherwise standard MRI exams of the lumbar spine.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This disclosure relates to systems, processors, devices, and methods formeasuring chemical constituents in tissue for diagnosing medicalconditions. More specifically, it relates to systems, pulse sequences,signal and diagnostic processors, diagnostic displays, and relatedmethods using novel application of nuclear magnetic resonance, includingmagnetic resonance spectroscopy, for diagnosing pain such as low backpain associated with degenerative disc disease.

2. Description of the Related Art

While significant effort has been directed toward improving treatmentsfor discogenic back pain, relatively little has been done to improve thediagnosis of painful discs.

Magnetic resonance imaging (MRI) is the primary standard of diagnosticcare for back pain. An estimated ten million MRIs are done each year forspine, which is the single largest category of all MRIs at an estimated26% of all MRIs performed. MRI in the context of back pain is sensitiveto changes in disc and endplate hydration and structural morphology, andoften yields clinically relevant diagnoses such as in setting ofspondlyolesthesis and disc herniations with nerve root impingement (e.g.sciatica). In particular context of axial back pain, MRI is principallyuseful for indicating degree of disc degeneration. However, degree discdegeneration has not been well correlated to pain. In one regard, peoplefree of back pain often have disc degeneration profiles similar to thoseof people with chronic, severe axial back pain. In general, not alldegenerative discs are painful, and not all painful discs aredegenerative. Accordingly, the structural information provided bystandard MRI exams of the lumbar spine is not generally useful fordifferentiating between painful and non-painful degenerative discs inthe region as related to chronic, severe back pain.

Accordingly, a second line diagnostic exam called “provocativediscography” (PD) is often performed after MRI exams in order tolocalize painful discs. This approach uses a needle injection ofpressurized dye in awake patients in order to intentionally provokepain. The patient's subjective reporting of pain level experiencedduring the injection, on increasing scale of 0-10, and concordancy tousual sensation of pain, is the primary diagnostic data used todetermine diagnosis as a “positive discogram”—indicating painfuldisc—versus a “negative discogram” for a disc indicating it is not asource of the patient's chronic, severe back pain. This has significantlimitations including invasiveness, pain, risks of disc damage,subjectivity, lack of standardization of technique. PD has beenparticularly challenged for high “false+” rates alleged in variousstudies, although recent developments in the technique and studiesrelated thereto have alleged improved specificity of above 90%. (Wolferet al., SPINE 2008) However, the significant patient morbidity of theneedle-based invasive procedure is non-trivial, as the procedure itselfcauses severe pain and further compromises time from work. Furthermore,in another recent study PD was shown to cause significant adverseeffects to long term disc health, including significantly acceleratingdisc degeneration and herniation rates (on the lateral side of needlepuncture). (Carragee et al., SPINE 2009). Controversies around PDremain, and in many regards are only growing, despite the on-goingprevalence of the invasive, painful, subjective, harmful approach as thesecondary standard of care following MRI. PD is performed an estimated400,000 times annually world-wide, at an estimated total economic costthat exceeds $750 Million Dollars annually. The need for a non-invasive,painless, objective, non-significant risk, more efficient andcost-effective test to locate painful intervertebral discs of chronic,severe low back pain patients is urgent and growing.

A non-invasive radiographic technique to accurately differentiatebetween discs that are painful and non-painful may offer significantguidance in directing treatments and developing an evidence-basedapproach to the care of patients with lumbar degenerative disc disease(DDD).

Previously reported lab experiments used 11 T HR-MAS Spectroscopy tocompare chemical signatures of different types of ex vivo disc nucleiremoved at surgery. (Keshari et al., SPINE 2008) These studiesdemonstrated that certain chemicals in disc nuclei, e.g. lactic acid(LA) and proteoglycan (PG), may provide spectroscopically quantifiablemetabolic markers for discogenic back pain. This is consistent withother studies that suggest DDD pain is associated with poor discnutrition, anaerobic metabolism, lactic acid production (e.g. risingacidity), extracellular matrix degradation (e.g. reducing proteoglycan),and increased enervation in the painful disc nuclei. In many clinicalcontexts, ischemia and lowered pH cause pain, likely by provokingacid-sensing ion channels in nociceptor sensory neurons.

The previous disclosures evaluating surgically removed disc samples exvivo with magnetic resonance spectroscopy (MRS) in a laboratory settingis quite encouraging for providing useful diagnostic tool based on MRS.However, an urgent need remains for a reliable system and approach foracquiring MRS signatures of the chemical composition of theintervertebral discs in vivo in a readily adoptable clinicalenvironment, and to provide a useful, clinically relevant diagnostictool based on these acquired MRS signatures for accurately diagnosingdiscogenic back pain. A significant need would be met by replacing PDwith an alternative that, even if diagnostically equivalent, overcomesone or more of the significant shortcomings of the PD procedure by beingnon-invasive, objective, pain-free, risk-free, and/or morecost-effective.

SUMMARY OF THE INVENTION

One aspect of the present disclosure is a MRS pulse sequence configuredto generate and acquire a diagnostically useful MRS spectrum from avoxel located principally within an intervertebral disc of a patient.

According to one mode of this aspect, the pulse sequence is configuredto generate and acquire the MRS spectrum from a single voxel principallylocated within the disc.

According to another mode of this aspect, the pulse sequence isconfigured to generate and acquire the MRS spectrum from the voxellocated principally within a nucleus of the disc.

According to another mode of this aspect, the pulse sequence isconfigured to generate and acquire the MRS spectrum with sufficientsignal-to-noise ratio (SNR) upon appropriate post-signal processing toperform at least one of: detect and measure at least one chemicalconstituent within the disc; and diagnose a medical condition based uponone or more identifiable signal features along the spectrum.

According to another mode, the pulse sequence is configured to generateand acquire the MRS spectrum from a single voxel principally locatedwithin a nucleus of the disc.

According to another mode, the pulse sequence is configured to generateand acquire the MRS spectrum from a voxel principally located within anintervertebral disc of the lumbar spine.

According to another mode, the pulse sequence is configured to generateand acquire at least one MRS spectrum from at least one voxelprincipally located within at least one of L3-L4, L4-L5, and L5-S1intervertebral discs.

According to another mode, the pulse sequence is configured to generateand acquire multiple MRS spectra from multiple voxels, respectively,principally located within each of L3-L4, L4-L5, and L5-S1intervertebral discs.

According to another mode, the pulse sequence is configured to generateand acquire multiple MRS spectra from multiple voxels, respectively,principally located within each of L3-L4, and L4-L5 intervertebraldiscs.

According to another mode, the pulse sequence is configured to generateand acquire the MRS spectrum from the voxel located principally withinthe L5-S1 intervertebral disc.

According to another mode, the pulse sequence is configured to generateand acquire the MRS spectrum via an NMR system of at least 1.5 tesla (T)field strength.

According to another mode, the pulse sequence is configured to generateand acquire the MRS spectrum via an NMR system of 1.5 tesla (T) fieldstrength.

According to another mode, the pulse sequence is configured to generateand acquire the MRS spectrum via an NMR system of at least 3.0 tesla (T)field strength.

According to another mode, the pulse sequence is configured to generateand acquire the MRS spectrum via an NMR system of 3.0 tesla (T) fieldstrength.

According to another mode, the pulse sequence comprises a chemical shiftselective (CHESS) sequence.

According to another mode, the pulse sequence comprises a point resolvedspectroscopy (PRESS) sequence.

According to another mode, the pulse sequence comprises a combinationCHESS-PRESS sequence.

According to another mode, the pulse sequence comprises at least onecontrol variable (CV) parameter setting as disclosed in Table 1.

According to another mode, the pulse sequence comprises all the controlvariable (CV) parameter settings disclosed in Table 1.

According to another mode, the pulse sequence comprises an echo time(TE) of about 28 milliseconds.

According to another mode, the pulse sequence comprises a repetitiontime (TR) of about 1000 milliseconds (1 second).

According to another mode, the pulse sequence comprises an acquisitionmatrix size setting of about 1 in each dimension, with a number ofspatial slices setting of 1.

According to another mode, the pulse sequence comprises at least one ofthe following CHESS flip angles: about 105 (angle 1); about 80 (angle2); about 125 (angle 3).

According to another mode, the pulse sequence comprises at least one ofthe following PRESS correction settings: about 1.2 for each of X, Y, andZ axes.

According to another mode, the pulse sequence comprises at least one ofthe following PRESS flip angles: about 90 (angle 1); about 167 (angle2); about 167 (angle 3).

According to another mode, the pulse sequence is configured to generateand acquire a repetitive frame MRS acquisition series from the voxelwith signal-to-noise ratio (SNR) in the water region along the spectrumof multiple said frames that is sufficiently high to be identified, yetsufficiently low to provide adequate dynamic range with sufficientsignal-to-noise ratio (SNR) along other chemical regions of diagnosticinterest along the spectral frames to allow the other regions to beidentified and evaluated, post-signal processing and post-averaging ofthe frames, for diagnostic use.

Another aspect of the present disclosure is an MRS signal processor thatis configured to select a sub-set of multiple channel acquisitionsreceived contemporaneously at multiple parallel acquisition channels,respectively, of a multi-channel detector assembly during arepetitive-frame MRS pulse sequence series conducted on a region ofinterest within a body of a subject.

According to one mode of this aspect, the MRS signal processor isconfigured to select a sub-set of multiple channel acquisitions receivedcontemporaneously at multiple parallel acquisition channels,respectively, of a multi-channel detector assembly during therepetitive-frame MRS pulse sequence series conducted on a voxelprincipally located within an intervertebral disc within the body of thesubject.

According to one mode of this aspect, the MRS signal processor isconfigured to automatically differentiate relatively stronger fromweaker channel acquisitions received.

According to another mode of this aspect, the MRS signal processor isconfigured to determine and select a strongest single channelacquisition signal among the multiple channel acquisitions.

According to one embodiment of this mode, the MRS signal processor isconfigured to determine and select the strongest single channelacquisition based upon a highest measured parameter of the singlechannel acquisition spectral series comprising at least one ofamplitude, power, or signal-to-noise ratio (SNR) of water signal in thespectrum in the selected channel relative to the other channel.

According to one variation of this embodiment, the selection is basedupon the frame averaged spectrum of the series acquired from thechannel.

According to another variation of this embodiment, the MRS signalprocessor is configured to determine and select a sub-set of strongestchannels based upon a range threshold based from the highest measuredparameter of the strongest single channel.

According to another embodiment, the MRS signal processor is configuredto determine and select one or more “strongest” channels among theseries based upon a threshold criteria for a feature of the channelacquisition data.

Another aspect of the present disclosure is an MRS signal processorcomprising a phase shift corrector configured to recognize and correctphase shifting within a repetitive multi-frame acquisition seriesacquired by a multi-channel detector assembly during an MRS pulsesequence series conducted on a region of interest within a body of asubject.

According to one mode of this aspect, the phase shift corrector isconfigured to recognize and correct the phase shifting within arepetitive multi-frame acquisition series acquired by a multi-channeldetector assembly during an MRS pulse sequence series conducted on avoxel within an intervertebral disc in the body of the patient.

According to another mode, the phase shift corrector is configured torecognize and correct the phase shifting in the time domain.

Another aspect of the present disclosure is a MRS signal processorcomprising a frequency shift corrector configured to recognize andcorrect frequency shifting between multiple acquisition frames of arepetitive multi-frame acquisition series acquired within an acquisitiondetector channel of a multi-channel detector assembly during a MRS pulsesequence series conducted on a region of interest within a body of asubject.

According to one mode of this aspect, the frequency shift corrector isconfigured to recognize and correct frequency shifting between multipleacquisition frames of a repetitive multi-frame acquisition seriesacquired within an acquisition detector channel of a multi-channeldetector assembly during a MRS pulse sequence series conducted on avoxel within an intervertebral disc in the body of the subject.

According to another mode, the frequency shift corrector is configuredto recognize and correct the frequency shifting in the time domain.

According to another mode, the frequency shift corrector is configuredto recognize and correct the frequency shifting in the frequency domain.

According to one embodiment of this mode, the frequency shift correctoris configured to identify and locate a water peak in each of multipleacquisition frames of the series, compare the location of the locatedwater peaks against a reference baseline location to determine aseparation shift therebetween for each frame, and to correct the shiftto align the location to the baseline location by applying anappropriate offset to all the spectral data of each frame.

According to one variation of this embodiment, the location of the waterpeak is estimated based upon a location range where the water signalexceeds a threshold amplitude value.

Another aspect of the present disclosure is a MRS signal processorcomprising a frame editor configured to recognize at least one poorquality acquisition frame, as determined against at least one thresholdcriterion, within an acquisition channel of a repetitive multi-frameacquisition series received from a multi-channel detector assemblyduring a MRS pulse sequence series conducted on a region of interestwithin a body of a subject.

According to one mode of this aspect, the frame editor is configured toedit out the poor quality frame from the series.

According to another mode, the frame editor is configured to recognizethe poor quality acquisition frame based upon a threshold value appliedto error in peak location of recognized water signal from an assignedbaseline location.

According to another mode, the frame editor is configured to recognizethe poor quality acquisition frame based upon a threshold confidenceinterval applied to the ability to recognize the peak location of watersignal in the frame spectrum.

Another aspect of the present disclosure is an MRS signal processor thatcomprises an apodizer configured to apodize an MRS spectrum otherwisegenerated and acquired by via an MRS aspect otherwise herein disclosed,and/or signal processed by one or more of the various MRS signalprocessor aspects also otherwise herein disclosed.

Another aspect of the present disclosure is an MRS diagnostic processorconfigured to process information extracted from an MRS spectrum for aregion of interest in a body of a subject, and to provide the processedinformation in a manner that is useful for diagnosing a medicalcondition associated with the region of interest.

According to one mode of this aspect, the MRS diagnostic processor isconfigured to process the extracted information from the MRS spectrumfor a voxel principally located in an intervertebral disc of thesubject, and to provide the processed information in a manner that isuseful for diagnosing a medical condition associated with theintervertebral disc.

According to one embodiment of this mode, the MRS diagnostic processoris configured to process the extracted information from the MRS spectrumfor a voxel principally located in a nucleus of the intervertebral disc,and to provide the processed information in a manner that is useful fordiagnosing a medical condition associated with the intervertebral disc.

According to another embodiment, the MRS diagnostic processor isconfigured to provide the processed information in a manner that isuseful for diagnosing the intervertebral disc as painful.

According to another embodiment, the MRS diagnostic processor isconfigured to provide the processed information in a manner that isuseful for diagnosing the intervertebral disc as severely painful.

According to another embodiment, the MRS diagnostic processor isconfigured to provide the processed information in a manner that isuseful for diagnosing the intervertebral disc as not severely painful.

According to another embodiment, the MRS diagnostic processor isconfigured to provide the processed information in a manner that isuseful for diagnosing the intervertebral disc as substantiallynon-painful.

According to another embodiment, the MRS diagnostic processor isconfigured to diagnose the disc as painful.

According to another embodiment, the MRS diagnostic processor isconfigured to diagnose the disc as severely painful.

According to another embodiment, the MRS diagnostic processor isconfigured to diagnose the disc as not severely painful.

According to another embodiment, the MRS diagnostic processor isconfigured to diagnose the disc as substantially non-painful.

According to another embodiment, the MRS diagnostic processor isconfigured diagnose the disc as not severely painful.

According to another embodiment, the MRS diagnostic processor isconfigured to assign a value for the disc that is referenced against arange for use in determining presence, absence, or level of pain.

According to another embodiment, the MRS diagnostic processor isconfigured to provide the diagnostically useful information in a displayoverlay onto an MRI image.

According to one variation of this embodiment, the display overlayassociates the diagnostically useful information with one or moreintervertebral discs evaluated.

According to another variation, the display overlay comprises a scaledlegend of values along a range, and an indicator of a result referencedagainst the range in the legend and associated with an intervertebraldisc evaluated.

According to another variation, the display overlay comprises both colorcoding and numerical coding of results in a legend and for at least oneindicator of processed information associated with at least oneintervertebral disc evaluated by the diagnostic processor.

According to another embodiment, the diagnostic processor comprises adiagnostic algorithm empirically created by comparing acquired andprocessed MRS spectra for multiple intervertebral discs against controlmeasures for pain, and that is configured to determine whether discsevaluated with the MRS spectra are painful or non-painful.

According to one variation, the diagnostic algorithm comprises at leastone factor related to spectral information extracted from MRS spectralregions associated with at least one of proteoglycan, lactate, andalanine chemicals.

According to one feature of this variation, the extracted informationrelated to at least one said region is divided by voxel volume.

According to another feature of this variation, the extractedinformation related to at least one said region comprises a peak valuein the region.

According to another feature of this variation, the extractedinformation related to at least one said region comprises a power valuein the region.

According to another applicable feature, the diagnostic algorithmcomprises at least two factors related to spectral information extractedfrom the MRS spectral regions associated with at least two of saidchemicals.

According to another applicable feature, the diagnostic algorithmcomprises three factors related to spectral information extracted fromthe MRS spectral regions associated with all three of said chemicals.

According to another applicable feature, the diagnostic algorithmcomprises at least two said factors related to spectral informationextracted from the MRS spectral regions associated with all three ofsaid chemicals.

According to still another applicable feature, at least one said factoris weighted by a constant.

According to another applicable feature, at least one said factorcomprises a ratio of at least two values associated with informationextracted from the MRS spectra at regions associated with at least twoof proteoglycan, lactate, and alanine chemicals.

According to still a further variation, the algorithm comprises fourfactors associated with MRS spectral data associated with proteoglycanregion, lactate region, proteoglycan:lactate region ratio, andproteoglycan:alanine region ratio.

According to one applicable feature of this variation, the algorithmcomprises four factors associated with MRS spectral data associated withproteoglycan region divided by voxel volume, lactate region divided byvoxel volume, proteoglycan:lactate region ratio, andproteoglycan:alanine region ratio.

According to still another applicable feature, the four factors areweighted by constants.

According to still a further variation, the algorithm is configured tocalculate a diagnostically useful value as follows:Value=−[log(PG/LA*(0.6390061)+PG/AL*(1.45108778)+PG/vol*(1.34213514)+LA/VOL*(−0.5945179)−2.8750366)];wherein PG=peak measurement in proteoglycan spectral region, AL=peakmeasurement in alanine region, LA=peak measurement in LA region, andvol=volume of prescribed voxel in disc used for MRS data acquisition.

According to still a further applicable feature, the calculateddiagnostically useful value is compared against a threshold value ofzero (0) to determine pain diagnosis.

According to still a further applicable feature, positive calculatedvalues are considered painful and negative calculated values areconsidered non-painful diagnoses.

According to another variation, the diagnostic algorithm is based atleast in part upon a feature associated with a combined spectral regionassociated with lactate and alanine chemicals.

According to another variation, the diagnostic algorithm is based atleast in part upon a power measurement taken along an MRS spectralregion that combines regions associated with lactate and alaninechemicals.

Another aspect of the present disclosure is an MRS system comprising anMRS pulse sequence, MRS signal processor, and MRS diagnostic processor,and which is configured to generate, acquire, and process an MRSspectrum for providing diagnostically useful information associated witha region of interest in a body of a patient.

According to one mode of this aspect, the MRS system comprising the MRSpulse sequence, MRS signal processor, and MRS diagnostic processor, isconfigured to generate, acquire, and process the MRS spectrum for avoxel principally located in an intervertebral disc in the body of thepatient and to provide diagnostically useful information associated withthe disc.

According to one embodiment of this mode, the voxel is principallylocated in a nucleus of the disc.

According to another embodiment of this mode, the diagnostically usefulinformation is useful for diagnosing pain or absence of pain associatedwith the disc.

Various further modes of this aspect are contemplated that comprise oneor more of the various aspects, modes, embodiments, variations, andfeatures of the MRS pulse sequence, MRS signal processor, and MRSdiagnostic processor as described above.

According to one such further mode, the MRS pulse sequence comprises acombination CHESS-PRESS sequence.

According to another such further mode, the MRS pulse sequence comprisesa TE of about 28 ms and a TR of about 1000 ms.

According to another such further mode, the MRS signal processorcomprises at least one of a channel selector, a phase shift corrector, afrequency shift corrector, and a frame editor.

According to another such further mode, the MRS diagnostic processor isconfigured to calculate and provide diagnostically useful informationfor diagnosing pain associated with at least one intervertebral discbased upon at least one MRS spectral region associated with at least oneof proteoglycan, lactate, and alanine chemicals.

According to another mode of the various aspects above, each or all ofthe respective MRS system components described is provided as user orcontroller operable software in a computer readable storage mediumconfigured to be installed and operated by a processor.

According to one embodiment of this mode, a computer operable storagemedium is provided and stores the operable software.

Still further aspects of the present disclosure comprise various MRSmethod aspects associated with the other MRS system, sequence, andprocessor aspects described above.

Each of the foregoing aspects, modes, embodiments, variations, andfeatures noted above is considered to represent independent value forbeneficial use, whereas their various combinations and sub-combinationsas may be made by one of ordinary skill based upon a thorough review ofthis disclosure in its entirety are further contemplated aspects also ofindependent value for beneficial use.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentdisclosure will now be described with reference to the drawings ofembodiments, which embodiments are intended to illustrate and not tolimit the disclosure.

FIGS. 1A-C illustrate an example of a voxel prescription within a discfor performing a DDD-MRS exam according to one aspect of the disclosure,in coronal, sagittal, and axial imaging planes, respectively.

FIG. 2 shows an example of the sectional deployment in a GE phased arrayspine coil with which certain aspects of the present disclosure areconfigured to interface for cooperative operation and use.

FIG. 3 shows an example of a CHESS water suppression pulse sequencediagram representing certain pulse sequence aspects contemplated bycertain aspects of the present disclosure.

FIG. 4 shows various different aspects of an exemplary combinedCHESS-PRESS pulse sequence diagram also consistent with certain aspectsof the present disclosure.

FIG. 5 shows Real (Sx) and imaginary (Sy) parts of an FID (right) thatcorrespond to x and y components of the rotating magnetic moment M(left).

FIG. 6 shows an amplitude plot of complex data from a standard MRSseries acquisition of multiple frame repetitions typically acquiredaccording to certain present embodiments, and shows amplitude of signalon the y-axis and time on the x-axis.

FIG. 7 shows a graph of an exemplary spectrum produced as the outputaverage after combining all of 6 activated acquisition channels andaveraging all frames, such as typically provided in display by a GESigna, and is pre-applying the various signal processing approaches ofthe present disclosure.

FIG. 8 shows a graphical display of individual channel spectra of alluncorrected channels of the same MRS acquisition featured in FIG. 7prior to combining the channels, and is also pre-processing according tothe signal processing approaches of the present disclosure.

FIG. 9 shows a schematic flow diagram of one DDD-MRS processorconfiguration and processing flow thereunder, first operating in DDD-MRSsignal processor mode by conducting optimal channel (coil) selection,then phase correcting, then apodizing, then transforming domain (fromtime to frequency), then editing out poor frames, then correcting forfrequency shifts, then averaging of all selected coils, and thenfollowed by DDD-MRS diagnostic processor & processing flow of dataextraction, then apply the algorithm, then generate a patient report.

FIG. 10 shows a plot of phase angle pre- and post-phase correction foran exemplary acquisition series for a disc such as that featured indifferent modes of spectral processing in FIGS. 6-9.

FIG. 11 shows the individual channel averages shown in FIG. 8, but afterphase correcting consistent with the signal processing flow shown inFIG. 9 and phase-correction approach illustrated in FIG. 10.

FIG. 12 shows the frame-averaged spectrum after combining the strongesttwo channels (channels 1 and 2) selected among the 6 phase-correctedframe-averaged channel spectra shown in FIG. 11 using a channelselection approach and criterion according to a further aspect of thecurrent disclosure.

FIG. 13 shows time-intensity plots of an exemplary series acquisitionfor a disc pre-(left) and post-(right) frequency correction according toa further aspect of the present disclosure, and shows each acquisitionframe as a horizontal line along a horizontal frequency range withbrightness indicating signal amplitude (bright white indicating higheramplitude, darker indicating lower), and shows the series of relatedrepetitive frames in temporal relationship stacked from top to bottome.g. top is time zero).

FIG. 14 shows the same time-intensity plots shown in FIG. 13 pre-(left)and post-(right) frequency correction, but in enhanced contrast format.

FIG. 15 shows spectral plots for 6 frame-averaged acquisition channelsfor the same acquisition shown in FIGS. 8 and 11, except post phase andfrequency correction and prior to optimal channel selection and/orcombination.

FIG. 16 shows a spectral plot for averaged combination offrame-averaged, phase and frequency corrected channels 1 and 2 selectedfrom FIG. 15.

FIG. 17 shows an exemplary time intensity plot for a DDD-MRS acquisitionsimilar to that shown in FIG. 13 (left), except for an acquisitionseries with corrupted frames.

FIG. 18 shows confidence in frequency error estimate vs. MRS framestemporally acquired across an acquisition series for a disc, as plottedfor the series acquisition shown in different view in FIG. 17.

FIG. 19 shows a frame by frame frequency error estimate of theacquisition series featured in FIG. 18.

FIG. 20 shows all 6 frame-averaged acquisition channels for the seriesacquisition conducted on the disc featured in FIGS. 17-19, prior tocorrection.

FIG. 21 shows phase, frequency, and frame edited spectral averagecombining channels 3 & 4 after optimal channel selection, for the sameseries acquisition featured in FIGS. 17-20.

FIG. 22 shows a bar graph of mean values, with standard deviation errorbars, of Visual Analog Scale (VAS) and Oswestry Disability Index (ODI)pain scores calculated for certain of the pain patients and asymptomaticvolunteers evaluated in a clinical study conducted using certainphysical embodiments of a diagnostic system constructed according tovarious aspects of the present disclosure.

FIG. 23 shows a Receiver Operator Characteristic (ROC) curverepresenting the diagnostic results of the diagnostic system used in theclinical study with human subjects featured in part in FIG. 22, ascompared against standard control diagnostic measures for presumed truediagnostic results for painful vs. non-painful discs.

FIG. 24 shows a partition analysis plot for cross-correlation of aportion of the clinical diagnostic results of the DDD-MRS system underthe same clinical study also addressed in FIGS. 22-23, based onpartitioning of the data at various limits attributed to differentweighted factors used in the DDD-MRS diagnostic processor, with “x” datapoint plots for negative control discs and “o” data point plots forpositive control discs, also shows certain statistical results includingcorrelation coefficient (R2).

FIG. 25A shows a scatter plot histogram of DDD-MRS diagnostic resultsfor each disc evaluated in the clinical study also addressed in FIGS.22-24, and shows the DDD-MRS results separately for positive control(PC) discs (positive on provocative discography or “PD+”), negativecontrol (NC) discs (negative on provocative discography or “PD−”, plusdiscs from asymptomatic volunteers or “ASY”), PD− alone, and ASY alone.

FIG. 25B shows a bar graph of the same DDD-MRS diagnostic results shownin FIG. 25A across the same subject groups, but shows the mean valueswith standard deviation error bars for the data.

FIG. 26 shows a bar graph of presumed true and false binary “positive”and “negative” diagnostic results produced by the DDD-MRS system forpainful and non-painful disc diagnoses in the clinical study, ascompared against standard control diagnostic measures across thepositive controls, negative controls (including sub-groups), and alldiscs evaluated in total in the study.

FIG. 27 shows diagnostic performance measures of Sensitivity,Specificity, Positive Predictive Value (PPV), Negative Predictive Value(NPV), and Global Performance Accuracy (GPA) for the DDD-MRS diagnosticresults in the clinical study.

FIG. 28 shows a bar graph comparing areas under the curve (AUC) per ROCanalysis of MRI alone (for prostate cancer diagnosis), MRI+PROSE (MRSpackage for prostate cancer diagnosis), MRI alone (for discogenic backpain or DDD pain), and MRI+DDD-MRS (for discogenic back pain or DDDpain), with bold arrows showing relative impact of PROSE vs. DDD-MRS onAUC vs. MRI alone for the respective different applications andindications.

FIG. 29 shows PPV and NPV for MRI alone and MRI+DDD-MRS for diagnosingDDD pain, vs. standard control measures such as provocative discography.

FIG. 30A shows a digitized post-processed DDD-MRS spectrum and certaincalculated data derived therefrom as developed and used for calculatedsignal-to-noise ratio (SNR) of the processed result.

FIG. 30B shows a digitized pre-processed DDD-MRS spectrum and certaincalculated data derived therefrom as developed and used for calculatedsignal-to-noise ratio (SNR) of the processed result.

FIG. 31A shows a scatter plot histogram of signal-to-noise ratio (SNR)for standard “all channels, non-corrected” frame averaged MRS spectraproduced by the GE Signa system for a subset of discs evaluated usingthe DDD-MRS pulse sequence in the clinical study, and the SNR for thesame series acquisitions for the same discs post-processed by theDDD-MRS processor, as such SNR data was derived for example asillustrated in FIGS. 30A-B.

FIG. 31B shows the same data shown in FIG. 31A, but as bar graph showingmean values and standard deviation error bars for the data within eachpre-processed and post-processed groups.

FIG. 31C shows a scatter plot histogram of the ratio of SNR valuescalculated post-versus pre-processing for each discs per the SNR datashown in FIGS. 31A-B.

FIG. 31D shows a bar graph of mean value and standard deviation errorbar of the absolute difference between post- and pre-processed SNRvalues for each of the discs shown in different views in FIGS. 31A-C.

FIG. 31E shows a bar graph of mean value and standard deviation errorbar of the ratio of post- to pre-processed SNR values for each of thediscs shown in different views in FIGS. 31A-D.

FIG. 31F shows a bar graph of the mean value and standard deviationerror bar for the percent increase in SNR from pre- to post-processedMRS spectra for each of the discs further featured in FIGS. 31A-D.

FIG. 32A shows a mid-sagittal T2-weighted MRI image of a patientevaluated under the clinical study of Example 1 and comparing thediagnostic results of the physical embodiment DDD-MRS system developedaccording to various aspects hereunder against provocative discographyresults for the same discs, and shows a color-coded, number-codeddiagnostic legend for the DDD-MRS results (on left of image) anddiscogram legend (top right on image) with overlay of the DDD-MRSresults and discogram results on discs evaluated in the patient.

FIG. 32B shows a mid-sagittal T2-weighted MRI image of another patientevaluated under the clinical study of Example 1 and comparing thediagnostic results of the physical embodiment DDD-MRS system developedaccording to various aspects hereunder against provocative discographyresults for the same discs, and shows a color-coded, number-codeddiagnostic legend for the DDD-MRS results (on left of image) anddiscogram legend (top right on image) with overlay of the DDD-MRSresults and discogram results on discs evaluated in the patient.

FIGS. 33A-C show three respective planar views of a very selectivesaturation (VSS) prescription for a voxelated acquisition seriesconducted via an MRS pulse sequence according to further aspectshereunder.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Certain aspects of the current disclosure relate to new & improvedsystem approaches, techniques, processors, & methods for conductingclinical magnetic resonance spectroscopy (MRS) on human intervertebraldiscs, in particular according to a highly beneficial mode of thisdisclosure for using acquired MRS information to diagnose painful and/ornon-painful discs associated with chronic, severe axial lumbar (or“low”) back pain associated with degenerated disc disease (or “DDDpain”). For purpose of clarity in this disclosure, the current aspects,modes, embodiments, variations, and features disclosed with particularbenefits for this purposed are generally assigned the label “DDD-MRS.”

Various aspects of this disclosure relate to highly beneficial advancesin three aspects useful in particular for conducting a DDD-MRS exam: (1)MRS sequence for generating & acquiring robust MRS spectra; (2) signalprocessor configured to improve signal-to-noise ratio (SNR) of theacquired MRS spectra; and (3) diagnostic processor configured to useinformation from the acquired & processed MRS spectra for diagnosingpainful and/or non-painful discs on which the MRS exam is conducted in aDDD pain patient.

Several configurations & techniques related to the DDD-MRS pulsesequence & signal processor have been created, developed, and evaluatedfor conducting 3 T MRS on human intervertebral discs for diagnosing DDDpain. A novel “DDD” MRS pulse sequence was developed & evaluated forthis purpose, and with certain parameters specifically configured toallow robust application of the signal processor for optimal processedfinal signals in a cooperative relationship between the pulse sequence &post-signal processing conducted. These approaches have been developed,used, and evaluated in clinic with a 3 Tesla (3 T) “Signa” MR systemcommercially available from General Electric (GE). Highly beneficialresults have been observed using the current disclosed applicationtechnologies on this MR platform, which alone is considered to proposesignificant benefit to pain management in the patients requiringdiagnosis. However, the current disclosure contemplates these aspectsbroadly applicable according to one of ordinary skill to a variety of MRplatforms commercially available or that may be developed by variousdifferent manufacturers.

In conducting the DDD-MRS sequence, a single three dimensional “voxel”is prescribed by an operator at a control consul, using 3 imaging planes(mid-sagittal, coronal, axial) to define the “region of interest” (ROI)in the patient's body for MR excitation by the magnet & data acquisitionby the acquisition channel/coils designated for the lumbar spine examwithin the spine detector coil assembly. The DDD-MRS pulse sequencepulses an applied & released magnetic field to the ROI, which causesunique bonds of various chemicals within the ROI to resonate atdifferent “signature resonant frequencies” across a range. Theamplitudes of frequencies at various locations along this range areplotted along a curve as the MRS “spectrum” for the ROI. This is doneiteratively across multiple acquisitions for a given ROI, typicallyrepresenting over 100 acquisitions, and often between about 200 andabout 600 acquisitions, such as between 300 and 400 acquisitions for agiven exam of a ROI. One acquisition spectrum among these iterations iscalled a “frame” for purpose of this disclosure. These multipleacquisitions are conducted in order to average their respective acquiredspectra/frames to reduce the amplitudes of acquired signal componentsrepresenting noise (typically more random or “incoherent” & thus reducedby averaging) while better maintaining the amplitudes of signalcomponents representing resonant chemical frequencies in the ROI(typically repeatable and “coherent” and thus not reduced by averaging).By reducing noise while maintaining true target signal, this process isthus conducted for the primary objective to increase SNR. Theseacquisitions are also conducted at various acquisition channels selectedat the detector coils, generally 6 channels corresponding with thelumbar spine area. The 3 T MRI Signa (GE) system, in standard operationconducting one beneficial mode of DDD-MRS sequence evaluated, isconfigured to average all acquired frames across all acquisitionchannels to produce a single averaged MRS curve for the ROI.

This unmodified approach has been observed to provide a relatively lowsignal/noise ratio, with low confidence in many results regarding dataextraction at spectral regions of interest, such as for example and inparticular regions associated with proteoglycan or “PG” (n-acetyl) andlactate or lactic acid (LA). Sources of potential error and noiseinherent in this imbedded signal acquisition & processing configurationof the 3 T Signa, as operated under the sequence used, were observed.These various sources of potential error or signal-to-noise ratio (SNR)compromise were determined to be correctable—either by altering certainstructures or protocols of coil, sequence, or data acquisition, or inpost-processing of otherwise standard protocols and structures used.Among these approaches, various post-processing approaches weredeveloped & observed to produce significantly improved & highlyfavorable results using otherwise un-modified GE Signa operationpre-processing. In particular, various improvements developed andapplied under the current post-signal processor disclosed hereunder havebeen observed to significantly improve signal quality and SNR.

The improvements advanced under the post-signal processor configurationsdisclosed hereunder include embodiments related to the following: (1)acquisition channel selection; (2) Phase correction; (3) Frequencycorrection; (4) Frame editing and (5) Apodization. While any one ofthese is considered highly beneficial, their combination has beenobserved to provide significantly advantageous results. Various examplesare provided to illustrate sources of error or “noise” observed, andcorrections employed to improve signal quality. Strong signals typicallyassociated with normal healthy discs were evaluated first to assess thesignal processing approach. Signals from the Signa that were consideredmore “challenged” for robust data processing & diagnostic use wereevaluated for further development to evaluate if more robust metabolitesignal can be elicited from otherwise originally poor SNR signals fromthe Signa.

Defining the Voxel (Voxel Prescription)

The current disclosure relates principally to “single voxel” MRS, wherea single three dimensional region of interest (ROI) is defined as a“voxel” for MRS excitation & data acquisition. The spectroscopic voxelis selected based on T2-weighted high-resolution spine images acquiredin the sagittal, coronal and axial planes. The patient is placed intothe scanner in a supine position, head first. The axial spine imagesacquired are in the oblique plane in order to better encompass the discof interest. This voxel is prescribed within a disc nucleus for purposeof using acquired MRS spectral data to diagnose DDD pain, according tothe present preferred embodiments. Typical voxel dimensions (Z-axis,X-axis, Y-axis) are 5 mm (thick) by 14 mm (width) by 16 mm (length),though may vary any or all of these dimensions by operator prescriptionto suit a particular anatomy. In general for DDD-MRS applicationevaluating disc nucleus chemical constituents, the objective for voxelprescription is to capture as much of the nuclear volume as possible(e.g. maximizing magnitude of relevant chemical signals acquired), whilerestricting the voxel borders from capturing therewithin structures ofthe outer annulus or bordering vertebral body end-plates (where lipidcontribution may be captured and may shroud chemical spectral regions ofinterest such as lactate or alanine, as further developed elsewherehereunder. In fact, the actual operation may not exactly coincide withacquiring signal from only within the voxel, and may include somebordering region contribution. This some degree of spacing between theborders and these structures is often desired. These objectives may bemore difficult to achieve for some disc anatomies than others, e.g.L5-S1 may be particularly challenging as frequently highly angulated,irregularly shaped, and collapsed as to disc height.

Accordingly, an initial prescription may not be appropriate, though maynot be known until the sequence is begun. Accordingly, further aspectsof the present disclosure contemplate a voxel prescription protocolwhich prescribes a first prescription, monitors results (either duringscan or after completion), and if a lipid signature or other suspectedsignal degradation from expected results is observed, re-prescribe thevoxel to avoid suspected source of contaminant (e.g. make the voxelsmaller or adjust its dimensions, tilt, or location) and re-run anadditional DDD-MRS acquisition series (retaining the signal consideredmore robust and with least suspected signal degradation suspected to bevoxel error). According to still a further mode, a pre-set protocol forre-prescribing in such circumstances may define when to accept theresult vs. continue re-trying. In one exemplary embodiment, the voxelmay be re-prescribed and acquisition series re-run once, or perhapstwice, and then the best result is to be accepted. It is to beappreciated, as with many technology platforms, that operator trainingand techniques in performing such user-dependent operations may berelevant to results, and optimal (or conversely sub-optimal) results maytrack skill levels and techniques used.

In most voxel prescriptions, the thickness is limited by the scanner'sability to generate the magnetic gradient that defines the Z-axis (axialplane) dimension. For example, a minimum thickness limit is pre-set to 4mm on the GE Signa 3 T. While such pre-set limits of interfacing,cooperative equipment & related software certainly result in limits onthe current application's ability to function in that environmentoutside of these limits, the broad aspects of the current disclosureshould not be considered so limited, and functionality may flourishwithin other operating ranges in cases where such other impartedlimitations may be released. An example of a single voxel prescriptionaccording to the three images is shown in FIGS. 1A-1C as follows. FIG.1A shows a coronal view oriented aspect of the voxel prescription. FIG.1B shows a sagittal view oriented aspect of the voxel prescription. FIG.1C shows an axial view oriented aspect of the voxel prescription.

The “DDD” MRS Pulse Sequence

The DDD-MRS pulse sequence shares certain similarities, though with somesignificant modifications defined hereunder, with another pulse sequencecalled “PROSE”. PROSE is primarily intended for use for diagnosingprostate cancer, and is approved for use and sale and available from GE.The DDD-MRS pulse sequence of the present embodiments, and PROSE forfurther reference, employ a sequence approach called Point RESolvedSpectroscopy. This involves a double spin echo sequence that uses a 90°excitation pulse with two slice selective refocusing radio frequency(RF) pulses, combined with 3D chemical shift imaging (CSI) phaseencoding gradients to generate 3-D arrays of spectral data or chemicalshift images. Due to the small size, irregular shape, and the highmagnetic susceptibility present when doing disc spectroscopy for DDDpain, the 3D phase encoding option available under PROSE is not anapproach typically to be utilized under the current disclosed version ofDDD-MRS sequence, and single voxel spectra are acquired by this versionof DDD-MRS. This can be accomplished by setting the user controlvariables (CVs) for the matrix acquisition size of each axis to 1 (e.g.,in the event the option for other setting is made available). Furtheraspects of pulse sequence approaches contemplated are disclosedelsewhere hereunder.

Coil and Patient Positioning

The primary source of MRS signals obtained from a Signa 3 T scanner,according to the physical embodiments developed & evaluated hereunderthis disclosure, are from the GE HDCTL 456 Spine Coil. This is a“receive-only” coil with twelve coils configured into six sections (FIG.2). Each section contains a loop and saddle coil. For lumbar (andthoracic) coverage, such as associated with lumbar DDD pain diagnosis,sections 4, 5, and 6 are deployed to provide six individual channelsignals.

Water and Lipid Signal Suppression

In another sequence called “PROBE” commercially available by GE, andwhich is a CSI sequence used for brain spectroscopy, the lipid/fatsignals are resolved through the use of long TE (144 ms) periods and 2dimensional transformations (2DJ). These acquisition and signalprocessing techniques are facilitated by the large voxel volumesprescribed in the brain as well as the homogeneity of the brain tissueresulting in narrow spectral line widths. In the prostate regiontargeted by PROSE, however, the voxel prescriptions are much smaller andit is often impossible to place the voxel so as to exclude tissues thatcontain lipid/fat. Therefore, two robust water and lipid suppressionapproaches are available and used, if warranted, in the PROSE sequence:“BASING” and “SSRF” (Spectral Spatial Radio Frequency). An even morechallenging environment of bordering lipid and reduced homogeneity hasbeen observed with the current DDD pain application where the currentROI within disc nuclei are closely bordered by vertebral bodies withbone marrow rich in lipid content. However, due both to the desire touse short TE times (e.g. 28 ms) for the current DDD pain application inlumbar spine, and the desire to observe MRS signatures of otherchemicals within disc nuclei that may overlap with lipid signalcontribution along the relevant DDD-MRS spectrum, these water/lipidsuppression approaches as developed for brain and prostate applicationare not necessarily optimized for DDD-MRS application in manycircumstances. While a SSRF suppression approach for lipid resonancesmay be employed in the DDD-MRS sequence, the narrow band RF pulserequired for this may require a long RF period and amplitude that willexceed the SAR level for the imager. Water suppression is provided by aCHESS sequence interleaved or otherwise combined in some manner with thePRESS sequence in order to provide appropriate results. Optimization ofthe residual water spectral line for frequency correction is done,according to on hightly beneficial further aspect, with the settingprescribed for the third flip angle. The angle is lowered to reduce thewater suppression function which increases the residual water spectralline amplitude. A particular flip angle for this purpose may be forexample about 125, though may be according to other examples betweenabout 125 degrees and about 45 degrees. This flip angle is anotherexample where some degree of customization may be required, in order tooptimize water signal for a given disc. As some discs may be moredehydrated or conversely more hydrated than others, the watersuppression may be more appropriate at one level for one disc, and atanother level for another disc. This may require some iterative setting& acquisition protocol to optimize, whereas the exemplary angledescribed hereunder is considered appropriate for most circumstances andmay be a pre-defined starting place for “first try.” For further clarityand understanding of the present embodiments, FIG. 5 shows an example ofa CHESS water suppression pulse sequence diagram, whereas FIG. 6 showsan example of a combined CHESS-PRESS pulse sequence diagram.

Outer Voxel Suppression

Another feature that is hereunder contemplated according to a furthermode of the DDD-MRS sequence is the use of very selective saturation(VSS) pulses for removal of signal contamination that may arise fromchemical shift error within the voxel as well as outside the selectedROI or voxel in the disc nuclei. In the default operating mode of oneDDD-MRS sequence approach sharing some similarities with PROSE, forexample, multiple pairs of VSS RF suppression bands are placedsymmetrically around the prescribed DDD-MRS voxel. The DDD-MRS sequenceaccording to this mode uses the VSS bands to define the DDD-MRS volume.It is believed that up to six additional VSS bands may be prescribed(each consisting of three VSS RF pulses) graphically in PROSE, with thegoal of reducing the chemical shift error that can occur within thevoxel as well as suppress excitation of out of voxel tissue during thePRESS localization of the voxel. According to some observations inapplying DDD-MRS to disc spectroscopy, these additional graphic VSSpulses were found to not significantly improve the volume selection.Accordingly, while they may provide benefit in certain circumstances,they also may not be necessary or even desired to be used in others.

As shown in FIGS. 33A-C, multiple VSS bands are placed around the voxelprescription in each plane to reduce out of voxel excitation andchemical shift error present during the PRES localization of the voxel.

PRESS Timing Parameters

For purpose of comparative reference, the echo time (TE) of about 130 msis believed to be the default selection typically used for PROSE dataacquisitions. This echo time is typically considered too long for discspectra due to the shorter T₂ relaxation times of the chemicalconstituents of lumbar intervertebral discs, leading to a dramaticdecrease in signal to noise in long echo PRESS spectra. Therefore ashorter echo time setting for the scanner, such as for example 28milliseconds, is generally considered more appropriate & beneficial foruse in the current DDD-MRS sequence & DDD pain application. A framerepetition time (TR) of for example about 1000 ms provides sufficientrelaxation of the magnetic dipoles in the ROI and leads to reasonableacquisition times and is believed to represent a beneficial compromisebetween short acquisition times and signal saturation at shorter valuesof TR. Other appropriately applicable timing values for PRESS spectraapplicable to the DDD-MRS sequence may be, for example: number of datapoints equal to about 1024, number of repetitions equal to about 300,and typical voxel size of 4×18×16 mm³. First, second, and third flipangles of PRESS for the current exemplary DDD-MRS sequence embodimentmay be for example 90, 167, and 167, respectively.

Summary of Exemplary User Control Variables (CV) for DDD-MRS Sequence

The foregoing disclosure describes various user controllable sequencesettings observed to be appropriate and of particular benefit for use inan exemplary DDD-MRS sequence according to the current disclosure andfor use for diagnosing DDD pain, as contemplated under the preferredembodiments hereunder. These are further summarized in Table 1 below.

TABLE 1 Exemplary CV Variables for DDD-MRS sequence for generating MRSspectra useful for post-processing & diagnosing DDD pain CV VariableValue TE (usec) 28000 TR (usec) 1000000 Acquisition Matrix Size 1Acquisition Matrix Size 1 Number of spatial slices 1 Water SuppressionMethod 1 CHESS Flip Angle 1 1050 CHESS Flip Angle 2 800 CHESS Flip Angle3 125 VSS Band Configuration 7 PRESS Correction - X axis 1.2 PRESSCorrection - Y axis 1.2 PRESS Correction - Z axis 1.2 Number of Frames300 PRESS Flip Angle 1 90 PRESS Flip Angle 2 167 PRESS Flip Angle 3 167PRESS Correction Function 0One or more of these may comprise modifications from similar settingsthat may be provided for PROSE, either as defaults or as user definedsettings for a particular other application than as featured in thevarious aspects hereunder this disclosure. These CV settings, in contextof use as modifications generally to a sequence otherwise sharingsignificant similarities to PROSE, are believed to result in a highlybeneficial resulting DDD-MRS sequence for the intended purpose of latersignal processing, according to the DDD-MRS signal processor embodimentsherein described, and performing a diagnosis of DDD pain in discsexamined (the latter according for example to the DDD-MRS diagnosticprocessor aspects & exemplary embodiments also herein disclosed).However, it is also appreciated that these specific settings may bemodified by one of ordinary skill and still provide highly beneficialresults, and are also contemplated within the broad intended scope ofthe various aspects of this present disclosure.

Data Acquisition

The signal detected in the MR spectrometer in the receiving “detector”coil assembly, after exposing a sample to a radio frequency pulse, iscalled the Free Induction Decay (FID). In modern MR spectrometers the MRsignal is detected using quadrature detection. As a result, the acquiredMR signal is composed of two parts, often referred as real and imaginaryparts of FID. The time domain FID waveform is shown in FIG. 5, whichshows the real (Sx) and imaginary (Sy) parts of an FID (right) thatcorrespond to x and y components of the rotating magnetic moment M.

FIDs are generated at the period defined by TR. Thus a TR of about 1000milliseconds, according to the exemplary embodiment described above,equals a rate of about 1 Hz (about one FID per second). The FID signalreceived from each coil channel is digitized by the scanner to generatea 1024 point complex number data set or acquisition frame. An MRS scansession consists of sixteen frames of unsuppressed water FIDs and up to368 frames of suppressed water FIDs, which together are considered anacquisition series. The unsuppressed water FIDs provide a strong watersignal that is used by the signal processing to determine which coils touse in the signal processing scheme as well as the phase informationfrom each coil. However, due to gain and dynamic range in the systemthese high water content unsuppressed frames do not typically provideappropriate resolution in the target biomarker regions of the associatedspectra to use them for diagnostic data purposes. The suppressed waterFIDs are processed by the DDD-MRS processor to obtain this spectralinformation, though utilizing the unsuppressed frames for certainprocessing approaches taken by the processor. FIG. 6 shows the plot ofall the FIDs obtained in an MRS scan, and is an amplitude plot ofcomplex data from a standard DDD-MRS acquisition with the y-axisrepresenting the magnitude of FID data and the x-axis representingserial frame count over time.

Data Transfer

The scanner generates the FIDs using the defined sequences to energizethe volume of interest (VOI), digitizes them according to the defineddata acquisition parameters, and stores the data as floating pointnumbers. A data descriptor header file (DDF) with all the aforementionedparameters along with voxel prescription data is appended to the data togenerate the archive file. Examples of parameters from the GE Signa DDFare shown below as follows:

-   -   BW=2000 (Bandwidth of complex sampled data, in Hz)    -   FS=2000 (Sample frequency rate, in complex samples per second)    -   TE=28 (Echo time, in mS)    -   TR=1000 (Repetition time, in mS)    -   NWUF=16 (Number of initial Water-Unsuppressed frames)

The archive file may then be transferred to another computer running anapplication, such as Matlab® R2009a (e.g. with “Image ProcessingToolbox” option, such as to generate time-intensity plots such as shownin various Figures hereunder), which opens the archive file. The Matlabapplication is programmable, and is further programmed to signal processthe acquired and transferred DDD-MRS information contained in thearchive file, such as according to the various signal processingembodiments hereunder. Other packages, such as “C,” “C+,” or “C++” maybe suitably employed for similar purpose. This application, subsequentlyreferred to as the DDD-MRS signal processor, parses informationpertinent to the signal processing of the data from the data descriptionheader, and imports the FID data acquired at each detector coil forsubsequent signal processing. It will be understood that the DDD-MRSsignal processor can be implemented in a variety of manners, such asusing computer hardware, firmware, or software, or some combinationthereof. In some embodiments, the DDD-MRS signal processor can comprisea computer processor configured to execute a software application ascomputer-executable code stored in a computer-readable medium. In someembodiments, the computer processor can part of a general purposecomputer. In some embodiments, the DDD-MRS signal processor can beimplemented using specialized computer hardware such as integratedcircuits instead of computer software.

Signal Processing

Upon the acquisition of all MRS data, the scanner will typically providethe operator with a spectral image that is the averaged combination ofall frames across all the 6 detection channels (coils). An example ofsuch a waveform is shown in FIG. 7, which shows a typicalscanner-processed spectral signal plot of combined, averaged channels.FIG. 8 shows the magnitude only (no correction) images of each of thesix channels which faun the output from the Signa system shown in FIG.7, and thus input to the DDD-MRS signal processor.

According to one highly beneficial mode, the DDD-MRS signal processor isconfigured to conduct a series of operations in temporal fashion asdescribed hereunder. While this configuration is considered highlybeneficial, these same or similar tasks may be performed in differentorder, as would be apparent to one of ordinary skill.

According to the current exemplary embodiment, the first operation ofthe DDD-MRS processor assesses the SNR of each coil. This is done todetermine which coils have acquired sufficiently robust signal to usefor data processing & averaging—the result may produce one single coilthat is further processed, or multiple coils later used combinationunder multi-coil averaging. In the majority of acquired signalsobserved, only a subset of the 6 lumbar acquisition coils weredetermined to be sufficiently robust for use. However, the standardsystem output averages all 6 coils. Accordingly, this filtering processalone—removing poor signal coils and working with only stronger signalcoils—has been observed to dramatically improve processed spectra fordiagnostic use. While various techniques may be suitable according toone of ordinary skill, and thus contemplated hereunder, according to thepresent exemplary embodiment the SNR is calculated by obtaining theaverage power in the first 100 data points (the signal) and the last 100points (the noise) of the unsuppressed water FID. The unsuppressed waterFIDs signals are used because of the strong water signal. The coilchannel with the greatest SNR, and channels within 3 dB of thatstrongest one, are preserved as candidates for multi-coilaveraging—other coil channels falling below this range are removed fromfurther processing.

Further to the exemplary present embodiment, a second operationconducted by the DDD-MRS processor is phase alignment. This is performedto support coherent summation of the signals from the selected coilchannels and the extraction of the absorption spectra. This is necessarybecause a systemic phase bias is present in the different coil channels.This systemic phase bias is best estimated by analysis of the 16 dataframes collected at the beginning of each scan without watersuppression. This operation, according to one exemplary mode, analyzesthe phase sequence of the complex samples and fits a polynomial to thatsequence. A first-order (linear) fit is used. This provides a betterestimate of the offset than simply using the phase of the first sample,as is often done. This is because eddy current artifacts, if present,will be most prominent in the first part of the frame. The offset of thelinear fit is the initial phase. Observation has indicated that thefirst 150 samples (75 mS at the typical 2000 samples-per-second rate)typically provide reliable phase data. The fit is performed on each ofthe 16 water-unsuppressed frames for each coil channel and the meanphase of these 16 is used to phase adjust the data for the correspondingcoil channel. This is accomplished by performing a phase rotation ofevery complex sample in each frame to compensate for the phase offset asestimated above, setting the initial phase to zero.

The offset of the linear fit is the phase bias with respect to zero andthe slope is the frequency error with respect to perfect center-tuningon the water signal. Only the offset portion of the curve fit is used tophase correct the data. An example of this is shown in FIG. 10, whichshows phase angle before and after phase correction. The phase anglesignal is shown as the dotted line. The solid line is the least squaresfit estimate. The dashed line is the phase and frequency correctedsignal, though the offset component is used to phase correct andfrequency correction is performed subsequently in the temporal processaccording to the present exemplary DDD-MRS processor embodiment. Theresults of phase correction for each of all the six channels is shown inFIG. 11, with channels 1-3 indicated from left to right at the top, andchannels 4-6 indicated from left to right at the bottom of the figure.The averaged spectrum of the selected, phase corrected channels(channels 1 and 2) is shown in FIG. 12.

Frequency Correction

During the course of a typical acquisition cycle (e.g. about 4 minutes),frequency errors can occur due to patient motion and changes insusceptibility (respiration, cardiac cycle etc). In this environmentwhere the acquired spectral signals “shift” along the x-axis betweenmultiple sequential frames in an exam series, their subsequent averagingbecomes “incoherent”—as they are mis-aligned, their averagingcompromises signal quality. Unless this is corrected to “coherently”align the signals prior to averaging, this error can result in anincrease in line width, split spectral peaks and reduced peak amplitudesfor diminished spectral resolution relative between signal peaksthemselves (as well as SNR). Accordingly, the DDD-MRS processor performsfrequency correction prior to averaging frames. This is performedaccording to one exemplary embodiment in the frequency domain. This isdone by transforming the time domain data for each frame into frequencydomain absorption spectra, locating the water absorption peaks, andshifting the spectrum to align them to an assigned center referencelocation or bin. Once shifted, the frame spectra are averaged in thefrequency domain to generate the corrected or “coherent” channelspectra. In another embodiment, the desired frequency shift correctionfor a frame may be applied to the time domain data for that frame. Thetime domain data for all the frames would then be averaged with thefinal average then transformed back to spectra. While the processes arelinear and thus not dependent upon sequence of operation, it is believedin some circumstances that the latter embodiment may present slightlyincreased spectral resolution. In difficult signal acquisitionsituations, some of the frames do not have sufficient signal quality tosupport frequency correction. More specifically, water signal isinsufficiently robust to accurately “grab” its peak with high degree ofconfidence. This circumstance is addressed by another operation of theDDD-MRS processor, frame editing, described in the next section.

Frequency error can be visualized using a time-intensity plot of theabsorption spectra of all the frames in an acquisition cycle. As shownin FIG. 13, each acquisition frame is represented by a horizontal line,with amplitude of signal intensity across the frequency spectrumindicated by brightness in grey scale (brighter shade/white designateshigher amplitude, darker signal intensity indicates lower relativeamplitude). The horizontal lines representing individual acquisitionframes are displayed in vertically “stacked” arrangement that followstheir temporal sequence as acquired, e.g. time zero is in the upper leftcorner and frequency incremented from left to right. The top 16 linesrepresent unsuppressed water frames, with the remainder belowrepresenting suppressed water acquisitions. The brightest portion ofeach line is reliably recognized as the water peak absorption, typicallythe strongest signal of acquired MRS spectra in body tissues. Further toFIG. 13, this plot for the original acquired sequence of frames from anacquisition series intended to be averaged is shown pre-frequencycorrection (e.g. with original frequency locations) on the left, andpost-frequency correction on the right. Shifting of the location of thisbright white water peak region, as observed between vertically stackedframes, indicates frequency shift of the whole MRS spectrum betweenthose frames—including thus the peaks of spectral regions of interestrelated to chemicals providing markers for pain. The rhythmic qualityobserved in this frequency shifting, per the alternating right and leftshifts seen around a center in the uncorrected plot (left side offigure) shift, remarkably approximates frequency of respiration—and thusis believed to represent respiration-induced susceptibility artifact.The contrasted plots seen in the pre and post frequency corrected timeintensity plots shown in FIG. 13 reveal the corrected “alignment” of thepreviously shifted signals for coherent averaging. For further clarity,an enhanced contrast image (FIG. 14) shows the original frequencyshifted, incoherent mis-alignment (left pane) and frequency corrected,coherent alignment (right pane) of the water peaks from this sameacquisition series. In this exemplary case shown in FIGS. 13-14, all ofthe frames were of sufficient quality to support frequency correction.

The frequency corrected absorption spectra for each acquisition cycleare averaged to generate an average frequency (and phase) correctedspectra for each channel, as is shown in FIG. 15. The selected channels(channels 1 and 2) are then averaged to produce the final spectra (FIG.16) used for extraction of data along spectral regions of interest thatare considered relevant to DDD pain diagnosis.

Frame Editing

While it is contemplated that in some circumstances individual MRSacquisition frames may provide some useful information, frame averagingis prevalently indicated in the vast majority of cases to achieve aspectrum with sufficient SNR and interpretable signal at regions ofinterest for pathology assessment. It is, at most, quite rare that anindividual frame will have sufficient SNR for even rudimentarymetabolite analysis to the extent providing reliable diagnosticinformation. Often individual frames along an acquisition series willhave such low SNR, or possess such artifacts, that they make noimprovement to the average—and in fact may even degrade it. To theextent these “rogue” frames may be recognized as such, they may beexcluded from further processing—with only robust frames remaining, theresult should improve.

Accordingly, a further mode of the present exemplary DDD-MRS processorembodiment utilizes frame editing to identify those frames which varysufficiently from the expected or otherwise observed acquisition resultssuch that they should be excluded. In one aspect of the underlyingconcern, certain patient motions during an acquisition may result insignal drop-out as well as frequency shifts (e.g. susceptibilityartifact). While involuntary motion, e.g. respiration, is a common causeof frequency shifts, these are typically sufficiently minor and within arange that they are not believed to implicate signal quality other thanthe shift itself (which can be corrected). However, other moresignificant movements (e.g. voluntary) may cause sufficientlysignificant shifts to seriously degrade the acquired spectrum (e.g. maymove the voxelated region to include adjacent tissues versus only theintended ROI upon prescription prior to the motion). If the salientartifact is frequency shift, a correction may be applied and the framecan be used to make a positive contribution to the averaged spectrum. Ifa frame is discarded its contribution is lost, and across sufficientnumber of discarded frames across a series the result may not include asufficient number of frames in the average for a reliable SNR in theresulting spectrum. The DDD-MRS processor, according to the currentexemplary embodiment, analyzes the residual water signal in each frameto determine if it is of sufficient quality to support frequencycorrection. FIG. 17 is a time-intensity plot which illustrates a scanseries with frequency shifts and “drop outs” with SNR changes consideredto represent corrupted frames due to patient motion. In this particularexample, after excluding the “drop out” frames (center of time sequencebetween about 75 and 175 MRS frames, it was still possible to obtain ahigh quality final averaged spectrum from this scan using the remainingrobust frames.

FIGS. 18 and 19 show the confidence in the frequency error estimate andthe frequency error, respectively, which are used according to thepresent exemplary embodiment for frame editing. More specifically, FIG.18 shows confidence in the frequency error estimate, with confidencelevel on the Y-axis, and the sequential series of frame acquisitionsalong a scan indicated along the X-axis. FIG. 19 shows the actualfrequency error along the Y-axis, for the same frame series along theX-axis, while the frequency error is reflected in both frequency domainand time domain contexts. This is based on analyzing the characteristicsof the residual water peak and the noise in a band 80 Hz wide (for 3 Tprocessing, it would be 40 Hz wide at 1.5 T) around the center-tunedfrequency. The largest peak is assumed to be the water signal and theassumption is qualified by the confidence estimate. If the confidencevalue is above 0.7, the frequency error estimate is considered valid andthe frame is flagged as a candidate for frequency correction. As seenfrom the plots, when the confidence is low, the variance of thefrequency error estimate is greatly increased. The final qualificationstep is to determine if there are enough qualified candidate frames toachieve sufficient SNR improvement when averaged. This threshold hasbeen empirically established as 90 frames, according to the presentexemplary embodiment, though other limits may be appropriate in variouscircumstances.

For further understanding & clarity re: the ultimate impact frameediting as described hereunder, the unprocessed power plot for all sixchannels from the patient with the compromised frames examined invarious views in prior Figures is shown in the FIG. 20, whereas thefrequency and phase corrected, and frame edited, spectra is shown inFIG. 21 (averaged channels 3 and 4).

The following documents are herein incorporate in their entirety byreference thereto:

-   1. Bottomley P A. Spatial localization in NMR spectroscopy in vivo.    Ann NY Acad Sci 1987; 508:333-348.-   2. Brown T R, Kincaid B M, Ugurbil K. NMR chemical shift imaging in    three dimensions. Proc. Natl. Acad. Sci. USA 1982; 79:3523-3526.-   3. Frahm J, Bruhn H, Gyngell M L, Merboldt K D, Hanicke W, Sauter R.    Localized high-resolution proton NMR spectroscopy using stimulated    echoes: initial applications to human brain in vivo. Magn Reson Med    1989; 9:79-93.-   4. Star-Lack J, Nelson S J, Kurhanewicz J, Huang L R, Vigneron D B.    Improved water and lipid suppression for 3D PRESS CSI using RF band    selective inversion with gradient dephasing (BASING). Magn Reson Med    1997; 38:311-321.-   5. Cunningham C H, Vigneron D B, Chen A P, Xu D, Hurd R E, Sailasuta    N, Pauly J M. Design of symmetric-sweep spectral-spatial RF pulses    for spectral editing. Magn Reson Med 2004; 52:147-153.-   6. Pauly J, Le Roux P, Nishimura D, Macovski A. Parameter relations    for the Shinnar-Le Roux selective excitation pulse design algorithm    [NMR imaging]. IEEE Trans Med Imaging 1991; 10:53-65.-   7. F. Jiru, Europeant Journal of Radialogy 67, (2008) 202-217    The following U.S. Patent Application Publications are herein    incorporated in their entirety by reference thereto: US2008/0039710    to Majumdar et al.; and US2009/0030308 to Bradford et al.

DDD-MRS Diagnostic Processor and Use for Diagnosing DDD Pain

Development, application, and evaluation of a DDD-MRS diagnosticprocessor configured for use for diagnosing DDD pain based upon DDD-MRSacquisition series acquired from discs according to a DDD-MRS pulsesequence and DDD-MRS signal processor applications is disclosed byreference to Example 1 below and according to other disclosure providedelsewhere hereunder. It will be understood that the DDD-MRS diagnosticprocessor can be implemented in a variety of manners, such as usingcomputer hardware, software, or firmware, or some combination thereof.In some embodiments, the DDD-MRS diagnostic processor can include acomputer processor configured to execute a software application ascomputer-executable code stored in a computer-readable medium. In someembodiments, the computer processor can be part of a general purposecomputer. The computer processor used by the DDD-MRS diagnosticprocessor can be the same computer processor used by the DDD-MRS signalprocessor, or it can be one or more separate computer processors. Insome embodiments, the DDD-MRS diagnostic processor can be implementedusing specialized computer hardware such as integrated circuits insteadof computer software.

Example 1

A DDD-MRS pulse sequence & signal processor were constructed toincorporate various aspects of the present embodiments disclosedhereunder and were used and evaluated in clinical experience across apopulation of discs in chronic, severe low back pain patients andasymptomatic control volunteers. Various data extracted from features ofinterest along the acquired & processed DDD-MRS acquisition series fordiscs evaluated in these subjects were compared against controldiagnoses for severe disc pain vs. absence severe disc pain, in order todevelop & characterize a DDD-MRS diagnostic processor with the highestpossible correlation to the control diagnoses.

Methods:

Clinical Study Population: The study included 65 discs from 36 totalsubjects. Thirty-eight discs were from 17 patients with a clinicaldiagnosis of chronic, severe low back pain (LBP group), and 27 discswere from 19 asymptomatic volunteers (ASY Group). 25 discs in 12 of theLBP patients also received PD (PD Group) sufficiently contemporaneouswith the DDD-MRS exam to provide appropriate comparison basis. All 65discs were evaluated for single voxel magnetic resonance spectroscopypulse sequence & data acquisition (DDD-MRS), and signal processorparameter development of the new DDD-MRS approach. 52 discs from 31subjects were considered appropriate and used as controls for developingand assessing the DDD-MRS diagnostic processor for diagnosticapplication of the overall DDD-MRS system and approach. Thirteendiscography positive (PD+) discs from the PD Group were used as positivecontrol (PC) discs, and 12 discography negative (PD−) discs from the PDGroup plus all the ASY discs were used as negative control (NC) discs. Abreakdown summary analysis of demographics among and between thesegroups is shown in Table 2.

TABLE 2 DDD-MRS Clinical Study Group Demographics & Comparison DDD-MRSClinical Study - Group Demographics Pain Patients Asymptomatics p valueBy SUBJECT (n = 31) n= 12 19 Male 7 (58%)  9 (47%) Female 5 (42%) 10(53%) Age 46.6 ± 9.4 32.4 ± 11.3 ** 0.0006 Height 68.3 ± 4.1 66.8 ± 4.5 0.1805 Weight 172.5 ± 38.5  151 ± 36.3 0.0639 BMI 25.9 ± 4.4 23.7 ± 3.990.0824 By DISCS (n = 52) n= 25 27 Male 16 (64%)  16 (59%) Female 9 (36%)11 (41%) Age  46.2 ± 9.04 35.2 ± 14.6 ** 0.0010 Height  68.7 ± 4.03 67.9± 4.5  0.2584 Weight 177.4 ± 39.3 157.6 ± 39.5  * 0.0381 BMI 26.2 ± 4.423.8 ± 4.3  * 0.0280 Pos. Controls Neg. Controls p value By DISCS (n =52) n= 13 39 Male 8 (62%) 24 (62%) Female 5 (38%) 15 (38%) Age   46 ±9.7 38.7 ± 13.9 * 0.0445 Height 68.9 ± 3.7 68.1 ± 4.4  0.2661 Weight182.4 ± 35.9  162 ± 40.8 0.0570 BMI 26.9 ± 4.2 24.4 ± 4.5  * 0.0402

Study Design: Standard lumbar MRI was performed on all subjects. PDperformed within the PD Group was conducted by discographers per theirdiscretionary techniques, and in all cases was performed blinded toDDD-MRS exam information. However, all PD+ criteria included: >=6 painintensity score concordant to typical back pain on PD; <=50 psi aboveopening pressure (where measured); and a negative control PD− disc inthe same patient (except one). All PD− discs had <6 pain intensityscores per PD. Pain questionnaires, including ODI and VAS, werecompleted by all subjects, with PD Group significantly higher than theASY Group according to both measures as shown in FIG. 22 (PD Group VAS &ODI on left side of graph, ASY Group VAS & ODI on right side of graph;VAS shown to left, ODI shown on right, within each group). The DDD-MRSpulse sequence and signal processor constructed according to the variouspresent embodiments hereunder was used for each series acquisition foreach disc, with data extracted from voxels prescribed at regions ofinterest within nuclei of all discs included in the study. A 3.0 T GESigna MRI system and 8-channel local spine detector coil were used withthe DDD-MRS package and approach (lower 6 of the 8 channels activatedfor lumbar signal acquisition). Information along spectral regions ofthe acquired DDD-MRS signals and associated with various chemicals ofinterest were evaluated against control diagnoses across the PC and NCgroups. Multi-variate logistic regression analyses were performed to fitthe dicotomous response (PC vs NC) to the continuous spectral measuresand develop a binary DDD-MRS diagnostic set of criteria and thresholdfor determining positive (MRS+) and negative (MRS−) pain diagnoses. Areceiver operator characteristic (ROC) curve was generated, and areaunder the curve (AUC) was calculated to assess the accuracy of thedeveloped test (FIG. 23). Five-fold cross-validation was performed toassess the generalizability of the predictive relationship (FIG. 24).

DDD-MRS diagnostic outcomes for each disc were based on a single numbercalculated via the developed set of criteria based upon four weightedfactors derived from regions of the acquired MRS signals and associatedwith three chemicals—PG, LA, and alanine (AL). It is noted, however,that LA and AL regions are relatively narrow and immediately adjacent toeach other, and in some cases the true respective signals representingthese actual chemical constituents may overlap with each other and/orinto the adjacent region's location. Furthermore, either or both of theLA and AL regions may also overlap with possible lipid contribution,which was believed to be observed in some cases (which may includesignal from adjacent tissues such as bone marrow of bordering vertebralbody/s). Positive numerical threshold results were assigned “MRS+” asseverely painful, and negative results were assigned “MRS−” as notseverely painful. Accordingly, the threshold for severely painful vs.otherwise non-painful diagnostic result is zero (0). The set ofdiagnostic criteria used to determine MRS+vs. MRS− diagnostic valuesaround this threshold with the most robust statistical correlation andfit to the control data observed across the disc population evaluatedfor this purpose is summarized as follows:Threshold=−[log(PG/LA*(0.6390061)+PG/AL*(1.45108778)+PG/vol*(1.34213514)+LA/VOL*(−0.5945179)−2.8750366)];wherein:

-   -   PG=peak measurement in PG region, AL=peak measurement in AL        region, LA=peak measurement in LA region, and vol=volume of        prescribed voxel in disc used for MRS data acquisition.

The distribution of DDD-MRS results according to these calculatedthresholds were compared against all PC and NC diagnoses, PD resultsalone, and portion of the NC group represented by the ASY group alone.Sensitivity, specificity, and positive (PPV) and negative (NPV)predictive values were also calculated per control comparisons.

Results:

DDD-MRS data demonstrated a strong correlation with the clinicaldiagnoses (R²=0.89, p<0.00001), with ROC analysis yielding an AUC of0.99 (FIG. 23) and cross-validation through partition analysis resultingin only deminimus variance in the R² (FIG. 24). Tables 3 and 4, andFIGS. 25A-27, show various aspects of the resulting clinical comparisondata for DDD-MRS vs. control diagnostic data, which data and comparisonsare further described as follows.

TABLE 3 Comparison of Clinical DDD-MRS Results (MRS+/−) vs. Positive &Negative Controls, per Disc DDD-MRS Results DDD-MRS Results PresumedTRUE Presumed FALSE % Match 3T Pain (All Disco) 23 2 92.0% 3T PosControl 12 1 92.3% (Pain, PD+) 3T Neg Control 11 1 91.7% (Pain, PD−) 3TNeg Control 27 0 100.0%  (Asymptomatic) 3T Neg Control 38 1 97.4% (All,PD− + Asymptomatics) 3T All 50 2 96.2%

TABLE 4 Comparison of Clinical DDD-MRS Results (MRS+/−) vs. Positive &Negative Controls, per Conventional Diagnostic Performance Measures:Sensitivity, Specificity, Positive Predictive Value (PPV), NegativePredictive Value (NPV), Global Performance Accuracy (GPA). DDD-MRSDiagnostic Performance Sensitivity 92.3% Specificity 97.4% PPV 92.3% NPV97.4% GPA 96.2%DDD-MRS results, with respect to binary MRS+ and MRS− diagnoses,correctly matched binary PC & NC diagnoses of painful/non-painful for50/52 (96.2%) discs evaluated across the PD and ASY groups. Of the 13MRS+ discs, 12 discs were from the PC group (PPV=92%). Of the 40 discsthat were MRS−, 39 were from the NC group (NPV=97%). DDD-MRS sensitivitywas about 92% and specificity was about 97%. Mean DDD-MRS results forthe PC and NC groups were 0.97±0.77 and −1.40±0.65 (R2=0.89, p<0.00001,FIG. 1). DDD-MRS results matched PD results in 23/25 (92.0%) discs ofthe PD Group: 12/13 (96.2%) PD+ and 11/12 (91.7%) PD−. Mean DDD-MRSalgorithm results for PD+ and PD− groups were 0.97±0.77 and −1.39±0.72(p<0.00001). DDD-MRS results correlated with PD pain intensity scores(R²=0.73). DDD-MRS results matched all 27/27 (100%) NC resultsrepresented by the ASY group. The mean DDD-MRS algorithm results for theASY group were −1.4±0.63, which differed significantly vs.PD+(p<0.0001), but were not significantly distinguishable vs. PD−results (p=0.46)(FIGS. 25A-B).

As shown in FIGS. 28-29, the DDD-MRS results according to this studyprovided highly favorable improvement vs. the diagnostic accuracytypically attributed to MRI alone for diagnosing painful vs. non-painfulDDD. More specifically, FIG. 28 (two bars on right side of graph) showsa comparison of the AUC for MRI alone vs. MRI+DDD-MRS, per meta analysisof previously reported AUC data for MRI for this indication. This isfurther compared in the graph against a recent study reporting AUC forMRI alone vs. MRI+PROSE for prostate cancer diagnosis (as compared tohistopathological diagnosis of biopsy samples), where no improvement wasshown by the additional inclusion of PROSE application of MRS within theMR-based diagnostic regimen. While the prostate data reflected withinthe graph reflects a larger relative population of samples inmulti-center study, and the DDD-MRS pain diagnostic results shownreflects a smaller population within single center experience, thedramatic relative improvement presented by the DDD-MRS approach in thesingle center experience is expected to carry over to a significantdegree into larger, multi-center context for this application. Furtherto FIG. 29, this additionally shows improvement to positive and negativepredictive values by enhancing standard MRI alone with the addition ofthe DDD-MRS diagnostic—per meta analysis of the current data vs.previously published data for MRI for this purpose.

Certain benefits provided by the DDD-MRS processor for post-processingacquired MRS signals were also evaluated across a sub-set sampling ofthe DDD-MRS data derived from the clinical population under this study.In particular, for each series acquisition the SNR of the processedDDD-MRS signals (“DDD-MRS spectra/spectrum”) was characterized, andcompared against the 6 channel average, non-phase or frequencycorrected, GE Signa output spectra as acquired “pre-processing”according to the present embodiments (e.g. “input combinedspectra/spectrum”). This SNR characterization and comparison exercisewas conducted as follows.

A freeware digitization program (WinDIG™, Ver 2.5, copyright 1996, D.Lovy)) was used to digitize both final DDD-MRS results and “screen shot”images. The “screen shot” images were reverse-imaged using MS Paintprior to digitization. The output of the digitizer program is an arrayof integers in a CSV file format. The CSV data files were imported toMicrosoft™ Excel™ and re-plotted as shown in FIGS. 3 and 4. A “region ofinterest” on the chemical shift (CS) axis (x-axis) pertaining tometabolite proteoglycan (PG, CS=2.11 PPM) was deemed to be the “signal”.A region of interest to the far right (CS=0.5 PPM) which would nottypically contain any spectral activity was deemed to be the “noise”. Inthe event there was not a significant spectral peak in the PG regionwhich is the often the case on pain patient discs, then thelactate/Lipid region of interest (CS=1.33 PP) was used as the signal.The “ranges of interest” were visually determined on both imagesresulting in sections of the data array. The SNR of a waveform isexpressed as:10*log₁₀ (RMS signal/RMS noise).

The RMS value was calculated by taking the sum of squares of the datasection, calculating the mean of the sum of squares, and then taking thesquare root of the mean. Since the spectra are power amplitude plots,the log base 10 of the ratio of the RMS values is then multiplied by 10to generate the SNR in dB.

For further understanding of this approach and examples of the digitizedspectra and information extracted therefrom, FIG. 30A shows a digitizedDDD-MRS spectral plot and accompanying SNR information, whereas FIG. 30Bshows similar views for a digitized pre-processed channel averagedoutput spectral plot and related SNR information for the sameacquisition series.

These pre- and post-processing SNR results are shown in FIGS. 31A-F.More specifically, FIG. 31A shows the calculated SNR for the pre- andpost-processed spectra, with significant majority of the pre-processedspectral SNR shown on the left side histogram distribution of the plotfalling below 5 (and also much of the data below 3), but with asignificant majority of the post-processed spectral SNR shown on theright side histogram distribution of the plot falling above 3 (allbut 1) and above 5 (all but just 2). A typical accepted SNR range forconfidently measuring chemical constituents from an MRS plot is in manycases over 5, though in many cases may be for any data over 3—such thatbelow these thresholds many believe the data interpretation should be“unquantifiable” or “immeasurable”. In such an application of thesethresholds, it is clear that a significant portion of data acquiredpre-processing according to the present embodiments is not generallyuseful for interpretting signal regions of interest, whereas these dataas post-processed hereunder become quite consistently useful. In fact,as shown in FIG. 31B, the average SNR across the signals evaluated forthis comparison exercise was: about 3 (e.g. well below 5)pre-processing, and about 13 (e.g. well above 5) post-processing(p<0.001). As per the ratio of post- vs. pre-processed signals furthershown in FIG. 31C, in all cases compared the post-processed signals werehigher SNR than pre-processing, generally along a range between 2 to 8times higher SNR (with only one point falling below 2× improvement,though still about 50% improved). As further shown in FIGS. 31D-F, themean absolute improvement was about 10 dB, the mean ratio improvementwas over 4×, and the mean % improvement was well over 300% in convertingfrom pre- to post-processed signals according to the presentembodiments.

Discussion:

The differentiation of painful and non-painful lumbar degenerative discsis an important goal in the accurate assessment of pain generators, andin guiding clinical management of patients with lumbar degenerative discdisease. The novel application of Magnetic Resonance Spectroscopydeveloped and evaluated under this study proposes a non-invasive,objective, and quantifiable measure of the chemical composition of thelumbar intervertebral disc. The MRS diagnostic algorithm developed andused in this study demonstrates a high degree of sensitivity inidentifying patients with a clinical assessment of lumbar discogenicpain and a positive discogram, and a high degree of specificity inidentifying levels that are not painful, without any false positiveresults observed in asymptomatics. This study developing, uniformlyapplying, and characterizing the DDD-MRS diagnostic approachretrospectively across the study population evaluated hereunder is quiteencouraging. Cross validation also performed on the results predicts theapproach is generalizable to broader population, as may be readilyconfirmed in additional prospective study in more subjects, as may beconducted by one of ordinary skill.

It is to be appreciated that the foregoing disclosure, including Example1, provides various aspects that are highly beneficial, new advancementsthat enhance the ability to perform clinically relevant MRS-basedexaminations for diagnosing DDD pain. Each of these aspects, takenalone, is considered of independent value not requiring combination withother aspects hereunder disclosed. However, the combination of theseaspects, and various sub-combinations apparent to one of ordinary skill,represent still further aspects of additional benefit and utility. Thefollowing are a few examples of these aspects, in addition to othersnoted elsewhere hereunder or otherwise apparent to one of ordinaryskill, which aspects nonetheless not intended to be limiting to otheraspects disclosed hereunder and are intended to be read in conjunctionwith the remaining disclosures provided elsewhere hereunder:

Channel Selection for Data Processing & Diagnosis:

Conventional MRI systems use multi-channel acquisition coils for spinedetectors, which are pads that patients lye upon during a scan. GE Signauses an 8 channel acquisition coil array, of which 6 channels aretypically activated for use for lumbar spine imaging & diagnosis(including for MRS). However, the system generally combines all datafrom these channels in producing a single “averaged” curve. For singlevoxel MRS, this has been determined to be highly inefficient andsignificant source of error in the data, in particular reducingsignal-to-noise ratio. The channels vary in their geographical placementrelative to lumbar discs, and are believed to be at least one source ofvariability between them regarding acquired signal quality for a givendisc. Of the six channels, most frequently at least one of the channelsis clearly “poor” data (e.g. poor signal-to-noise), and often this canmean 2 to 5 of those channels being clearly degraded vs. one or more“strong” channels. Accordingly, the present disclosure contemplates thatcomparing the channels, and using only the “strongest” channel(s),significantly improves signal quality and thus data acquired andprocessed in performing a diagnosis. This “channel isolation/selection”is considered uniquely beneficial to the DDD pain applicationcontemplated hereunder, and can be done manually as contemplatedhereunder, though the present disclosure also includes automating thisoperation to compare and choose amongst the channels for a given voxelscan via an automated DDD-MRS signal processor disclosed.

“Coherent” Averaging within & Between Channels:

During a single voxel scan, many repetitions are performed that arelater used for averaging in order to reduce noise and increasesignal-to-noise ratio in an acquired MRS spectrum. This can range fromabout 100 repetitions to about 600 or more, though more typically may bebetween about 200 to about 500, and still more frequently between about300 to about 400, and according to one specific exemplary embodimentfrequently included in the physical embodiments evaluated in theclinical study of Example 1 may be about 384 repetitions. With a TR of 1to 2 seconds for example, this can range from less than 5 to 10 minutestime.

However, a “shift” in phase and frequency has been observed among theacquired data over these repetitions. The current standard MRI systemconfigurations, via certain sequence routines, do not correct for suchshifts. Thus when these repetitions are averaged the result becomes“blurred” with reduced signal amplitude relative to noise, as well aspossibility for signal “broadening” or separation into multiple peaksfrom what should be otherwise a single, more narrow band peak.

In addition or alternative to “strongest” channel selection forprocessing, significant benefit and utility is contemplated hereunderfor correcting for one or both of these phase and/or frequency “shifts”among the repetitions of an acquisition series acquired at a channelduring a single voxel scan. The observed results of such processing havebeen higher signal quality, with higher signal-to-noise ratio, and/ormore narrow defined signals at bands of interest to spectral regionsassociated with chemicals believed (and correlated) to be relevant fordiagnosing disc pain (e.g., PG and/or LA and/or AL). It is noted, andrelevant to various of the detailed embodiments disclosed hereunder,that the spectral peak region associated with water is typically themost prominent and highest amplitude signal across the spectrum. Thispeak and its location relative to a baseline is used according tocertain of the present embodiments to define a given shift in a signal,and thus that shift at the water region is used to correct the entirespectral signal back to a defined baseline. As water peak shifts, orconversely is corrected, so does the rest of the spectrum including thetarget chemical markers relevant to conducting diagnoses.

This degree and location of the water peak may also be used to determineand edit acquisition frames which are sufficiently abnormally biasedrelative to the other acquisition frames to adversely impact spectraldata (or unable to “grab and shift”), e.g. frame editing according tofurther embodiments.

Where water is not as prominent, e.g. highly desiccated discs with oversuppressed water in the sequence, other reliably prominent andrecognizable peaks maybe identified used for similar purpose (e.g. peakswithin the PG and/or LA and/or AL regions themselves). However, due toits typical prominence and many benefits of using the water peak forthese various signal processing purposes, novel approaches and settingsfor water suppression are contemplated and disclosed hereunder. Thisprovides for a water signal, either manually or automatically, within anamplitude range that is sufficient to locate and “grab” for processing,but not so extensive to “washout” lower chemical signatures in aninappropriate dynamic range built around the higher water signal. Theresult of corrections contemplated hereunder aligns the repetitions tophase and/or frequency coherence, and thus the resulting averagingachieved is desirably more “coherent” averaging. It is furthercontemplated that these shifts may be observed and corrected in eithertime or frequency domain (esp. re: frequency shift), and while certainexemplary embodiments are described hereunder in detail correctionsyielding similarly improved results may be made in either domain (againesp. re: frequency coherent correction).

DDD-MRS Factors, Criteria & Thresholds for Diagnostic Results

The present disclosure provides an empirically derived relationshipbetween four weighted factors that involve data derived from threeregions of MRS spectra acquired from discs that are generally associatedwith three different chemicals, namely PG, LA, and AL. Other supportexists to suspect these identified chemicals may be active culprits indisc pain, e.g. reducing PG, and increasing LA and AL, as factored inthe diagnostic relationship developed and applied hereunder. Moredirectly, at least a sub-set of these factors used in this diagnosticdeveloped relationship have been directly correlated to disc pain (e.g.PG/LA ratio per prior 11 T studies performed ex vivo). These factors arefurther addressed in view of further supporting literature anddisclosures, which are believed to support their correlation to pain, asfollows.

The normal intervertbral disc is avascular and disc cells function underanaerobic conditions. (Ishihara and Urban 1999; Grunhagen, Wilde et al.2006) Anaerobic metabolism, such as in the setting of oxygen deprivationand hypoxia, causes lactate production. (Bartels, Fairbank et al. 1998;Urban, Smith et al. 2004) Disc pH is proportional to lactateconcentration. (Diamant, Karlsson et al. 1968) Lactic acid produces painvia acid sensing ion channels on nociceptors. (Immke and McCleskey 2001;Sutherland, Benson et al. 2001; Molliver, Immke et al. 2005; Naves andMcCleskey 2005; Rukwied, Chizh et al. 2007) Disc acidity has beencorrelated with pre-operative back pain. (Diamant, Karlsson et al. 1968;Nachemson 1969; Keshari, Lotz et al. 2008)

Proteoglycan content within the nucleus pulposus, which is the primarymatrix which holds water in the disc nucleus, decreases with discdegeneration, which is also associate with dehydration e.g. via“darkened” disc nuclei seen on T2 MRI. (Roughley, Alini et al. 2002;Keshari, Lotz et al. 2005; Keshari, Zektzer et al. 2005; Roberts, Evanset al. 2006) Chondroitin sulfate proteoglycans inhibit nerve ingrowth.(Zuo, Hernandez et al. 1998; Zuo, Neubauer et al. 1998; Jones, Sajed etal. 2003; Properzi, Asher et al. 2003; Jain, Brady-Kalnay et al. 2004;Klapka and Muller 2006) Nerve ingrowth is increased in degenerativepainful discs. (Brown, Hukkanen et al. 1997; Coppes, Marani et al. 1997;Freemont, Peacock et al. 1997; Freemont, Watkins et al. 2002)

Discography is the current gold-standard of diagnostic care fordifferentiating painful discs, but is controversial due to being:invasive, painful, subjective, technique/operator dependent, frequentlychallenged due to high false positive rates (principally as indicated instudies with asymptomatic volunteers), and risky to the patient.(Carragee and Alamin 2001; Guyer and Ohnmeiss 2003; O'Neill andKurgansky 2004; Cohen, Larkin et al. 2005; Carragee, Alamin et al. 2006;Carragee, Lincoln et al. 2006; Buenaventura, Shah et al. 2007; Wichman2007; Derby, Baker et al. 2008; Scuderi, Brusovanik et al. 2008; Wolfer,Derby et al. 2008) The prevailing modern guidelines for performingdiscography generally require concordant pain intensity scores equal toor above 6 (on increasing scale of 0-10), provocation pressures of nomore than 50 psi above opening pressure, and another negative controldisc in order to determine a “positive discogram” result for a disc.This modern technique has been most recently alleged to provide a higherspecificity (e.g. lower false positive) rates than previously alleged inother studies. (Wolfer et al., SPINE 2008) However, notwitsthanding thispotential improvement with modern techniques in the test's accuracy, amore recent published study has shown the invasive needle puncture ofdiscography significantly increases disc degeneration and herniationsrates. Further to this disclosure, these adverse affects of thediscography needle puncture in the “negative control discs” have beenalleged as possible culprit in adjacent level disc disease that oftenaffects adverse outcomes following surgical treatments removing the“positive discogram” discs (e.g. fusion and/or disc arthroplasty).

Proteoglycan and lactate within discs have unique MR signatures that canbe identified and objectively measured using MR Spectroscopy, and acalculated ratio based on these measures significantly differentiatespainful from non-painful discs. (Keshari, Lotz et al. 2008) DDD-MRSapproaches, as disclosed hereunder, can non-invasively, painlessly, andobjectively measure and quantify proteoglycan and lactate-relatedsignatures of intervertebral discs in vivo using a novel softwareupgrade to commercially available MRI systems, and a novel diagnosticalgorithm based at least in part upon these in vivo measures reliablydistinguishes painful vs. non-painful discs with a lower false positiverate predicted versus discography.

The following publications are herein incorporated in their entirety byreference thereto, and provide at least in part a bibliography ofcertain disclosures referenced above and otherwise elsewhere hereunder:

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Notwithstanding the foregoing, it is to be appreciated that despite thesupport for suspecting these chemicals as the cause of pain, and despitethe belief that these chemicals are measured and represented at least inpart by the data derived from the MRS data acquired, this correlationneed not be accurate in order for the data and diagnostic algorithm &approach presented hereunder to remain valid and highly useful.

In particular regard to MRS data derived from regions associated with LAand AL, these are quite narrowly defined ranges closely adjacent to eachother, and also overlap with a much broader band associated with lipid.Accordingly, the data acquired from these two “bins” may blur betweenthe actual two chemical sources. However, as they both relate to and area product of abnormal cellular metabolism and hypoxia, their combinationmay be fairly considered a signature region more broadly for “abnormalcellular metabolism/hypoxia.” Furthermore, lipid contribution may biasmeasurements in this region, and as lipid is a high molecular weightmolecule if present the signal is typically strong and often may washout resolution of either or both of LA or AL-based signal in the region.However, in the current experience with DDD-MRS, even where lipid signalis believed present, and even in significant degree, the acquired dataintended to represent LA and AL as processed through the diagnosticalgorithm and processor has not produced a false result against controls(e.g. remains an accurate result). When this happens, the diagnosticresult is consistently MRS+ indicating a positive result for pain in thesuspect disc. However, such lipid-related positive results occur mostfrequently in L5-S1 discs that are associated with a particulardegenerative profile and morphology that is more reliably diagnosed aspainful on MRI alone (and consistently confirmed as such via PD).

To the extent the measurements derived from the MRS “regions” believedto be associated with these chemicals, and as used in the weightedfactor diagnostic algorithm developed, are applied uniformly across thedifferent control disc populations, the diagnostic accuracy of theresult prevails in the ultimate comparison data—regardless of the sourceof the MRS data acquired. Accordingly, the benefit and utility of thediagnostic approach is defined ultimately by its diagnostic results, andnot intended to be necessarily limited and defined only by the theory asto what the underlying sources of the measured signatures are.

Conversely, it is also further contemplated and to be understood thatthe present disclosure provides a specific diagnostic relationshipalgorithm that produces a particular range of diagnostic results thatcompare with high correlation with control measures for pain/non-pain indiscs evaluated. However, this is the result of statistically generatedcorrelation and retrospective approach to data fitting. Whileappropriate for diagnostic algorithm development and the specific resultdisclosed hereunder is considered highly beneficial, this may migrate toother specific algorithms that may be more preferred though withoutdeparting from the broad scope intended for the various aspects of thisdisclosure. Such modifications may be the result of further dataprocessing across more samples, for example, and may affect the“weighting” multipliers associated with each factor used in thealgorithm, or which factors are featured in the algorithm, or whichregions or features of the MRS spectra are even used as the signaturesfrom which data is derived and used in the algorithm.

It is contemplated that while the DDD-MRS diagnostic processor hereunderdisclosed and diagnostic results provided therefrom, as disclosed incontext of clinical data presented under Example 1, provide binary MRS+and MRS− results for severe pain & absence of severe pain in discs,respectively. However, the results are also quantified along a scaledrange which may be appropriately interpreted by a diagnostician as“levels” of relevance along the pain/non-pain range. Such interpretationmay impact the direction of pain management decisions, such as whichdiscs to treat, how to treat, or not to treat at all. Moreover, whilethe current diagnostic embodiments have been described by reference tosite specific location of pain source at reference disc(s), diagnosticvalue may be more generalized to confirmed presence or absence of anypainful disc at all. Such may impact more general management decision,such as administration or avoidance of pain medication.

Furthermore, in still further embodiments, the diagnostic results may beprovided in different forms than as described by the specificembodiments disclosed by reference to Example 1. For example, binarydefinitive diagnoses of MRS+ and MRS− may be supplemented with“indeterminate” as a third category. This may, for example, represent aresult of applying certain threshold criteria that must be met in orderto make a definitive +/−determination. Such criteria may include, forexample, SNR threshold of the underlying post-processed DDD-MRS spectrumfrom which the diagnostic data is extracted for performing thediagnoses. In another example, a defined proximity of calculateddiagnostic results from the DDD-MRS diagnostic processor to the zero (0)median threshold between MRS+ and MRS− diagnoses may represent athreshold under which definitive MRS+/− determination is not decidedlymade by the processor.

It is also to be further appreciated that the pulse sequence platformapproach, and/or specific parameter settings, and/or signal processingapproaches (and/or parameter or threshold criteria settings), may bemodified. Such modifications may affect resulting spectra (and dataextracted therefrom) sufficiently to redistribute the regional data usedfor diagnostic purposes, and may thus motivate or necessitate are-evaluation and re-formation of the diagnostic algorithm that isappropriate for data acquired and/or processed under those modifiedapproaches. Accordingly, while the present interactions between thesecomponent parts of an overall DDD-MRS system, and results, areconsidered of particular benefit for forward application in clinicaluse, such further modifications are also considered to fall within thebroad scope of the aspects disclosed hereunder, and may represent forexample a consequence of further development and experience as would beapparent to one of ordinary skill (though such further modifications mayalso provide still further benefit).

L5-S1 & Novel Detection Coils:

The L5-S1 disc is typically oriented at an oblique angle relative toother lumbar discs, and has unique shape that in many circumstanceschallenges the ability to prescribe voxel for adequate DDD-MRS dataacquisition. The current voxelation plan for MRS generally requires athree-dimensional “cube” of space to be defined as the voxel (a pixelwith volume), typically done by an operator technician on overlay to MRIimages of the region. However, for this angled L5-S1 disc, the voxelvolume may be maximized by angling the voxel to match the angulateddisc. However, such angled voxels at this location have been observed torelate to degraded data acquisition by existing spine detector coils.Accordingly, a custom spine coil is further contemplated that angles atleast one coil channel to either a pre-determined angle morerepresentative of typical L5-S1 discs, or a range of angles may beprovided my multiple such coils in a kit, or the coil channel may begiven an “adjustable” angle to meet a given anatomy. Furthermore,software may be adapted to identify an angled voxel and modify thecoordinate system assigned for sequence and/or multi-channel acquisitionin order better acquire data from an angled voxel (e.g. where planarslices are taken through the voxel as data acquired, the planarcoordinates are revised into an adjusted coordinate system that accountsfor the angulation relative to the data acquisition at the channel(s)).This uniquely angled disc level is also associated with and locatedwithin a radiused curvature at the small of the back, which may be moreextreme in some patients than others. While simply adjusting the angleof lower detection channel coils may improve acquisition here, furthermore dramatic variations are also contemplated. In one such furtheraspect, a detector coil array is created with smaller coils, and/or on aflexible platform that is adjusted to more accurately fit against thelower back (vs. a planar array currently used, but for curved lowerspine with increasingly angulated discs toward the lower lumbar andsacral regions). Further to this approach, the relative locations andorientations of the detector coils may be sensed, with proper coordinatesystem assigned thereto for sequencing and acquisition during singlevoxel MRS of the spine (especially intervertebral discs), and which alsomay be adapted relative to coordinates of voxel orientation, dimensions,and shape.

T1-Rho:

An additional MRI-based pulse sequence technology has been previouslydisclosed called “T1-Rho”. This is a sequence that has been alleged fordetecting, measuring, and indicating the amount (e.g. concentration) ofproteoglycan, via n-acetyl or n-acetyl acetate, in tissue, andfurthermore for using this information for diagnostic benefit for someconditions. In one particular regard, this has been alleged to bepotentially useful for monitoring degree of degeneration, in thatreduced proteoglycan in discs may correlate to advancing degree ofdegeneration. While pain correlation with proteoglycan variability hasnot been determined, the ration of PG to other metabolites, such as forexample Lactate (and/or alanine), is believed to be a consistent andpotent indicator for localized discogenic pain. Accordingly, the presentdisclosure combines T1-Rho with other measurements, e.g. MRSmeasurements, in evaluating tissue chemistry for purpose of performing adiagnosis. In one particular mode contemplated hereunder, the T1-Rhomeasurement of proteoglycan/n-acetyl content is used to “normalize” orotherwise calibrate or compare an MRS measurement of that relatedregion. In doing so, other metabolites in the MRS spectrum may be alsocalibrated for more accurately calculated “concentration” measurement.This calibration may be done in evaluating MRS signal quality, such asfor example between channels or within a channel itself, and MRS data isused for the diagnosis. In a further mode, T1-Rho information related toPG may be used as the data for that chemical constituent in tissue, anddata for another diagnostically relevant chemical, e.g. Lactate asmeasured for example via MRS (or other modality), may be used incombination with the PG measurement in an overall diagnostic algorithmor evaluation. Such algorithms applied for diagnostic use may beempirically driven based upon experimental data which may be conductedand acquired by one of ordinary skill for such purpose based upon thisdisclosure. For example, a database of sufficient patient data based onT1-rho measurements (for proteoglycan) and MRS measurements (such as forPG and/or Lactate, for example) may be correlated in a multi-variatelogistic regression analysis against other pain indicators such asprovocative discography or treatment outcomes, resulting in a highlycorrelative algorithm based upon the data fit. This may then be usedprospectively in predicting or assessing localized pain in newlyevaluated patient tissues. In one particular benefit, MRS techniquesinclude particular sequence parameters that emphasize lactate forimproved lactate-related data extraction, and decreasing lipid artifact(which often overlays over lactate to confound lactate data collection),but not considered as robust for other chemicals, such as potentiallyPG/n-acetyl. One such technique extends the time delay from magneticactivation to data collection, thus increasing overall time forrepetitive scans. However, T1-Rho is relatively fast to perform relativeto MRS. Accordingly, one particular further embodiment uses T1-rho forPG measurement, and MRS as enhanced for lactate measurement, andcombines this data into an empirically data-driven algorithm forperforming a diagnosis. Moreover, a further aspect contemplatedhereunder uses T1-rho for PG measurement, in combination with pH or pO2measurement (e.g. via a sensor on a needle, such as a discographyneedle) to monitor local acidity in the disc (also believed to relate tolactate concentration).

Diagnostic Display “Enhancing” MRI Images

The various aspects, modes, and embodiments of the present disclosureprovide, among other beneficial advancements, a significant enhancementand improvement to standard MRI for locally diagnosing painful and/ornon-painful discs. The utility of each of these diagnoses—painful, andnon-painful—is of independent value on its own. While indicating a discis definitively painful may often augment other clinical or diagnosticindications for directing treatment to the level, indicating a disc isdefinitively not painful also provides valuable information to exclude adisc as possible pain culprit and avoid unnecessary intervention to thelevel (especially where other clinical or diagnostic indications mayindicate another level as painful, but not provide definitive answer tothe other level/s). This is for example often the case with respect toL3-L4 and L4-L5 discs, where L5-S1 discs (most prevalently painful amongthe levels) may often be already suspect per MRI and other indications,but the higher adjacent disc levels are indeterminate.

The present aspects have been presented in terms of physical embodimentsevaluated in clinical study with highly accurate results againstcontrols. By providing a non-invasive alternative to discography aspresented by these present embodiments, even if diagnosticallyequivalent, significant benefits are advanced by avoiding morbidity,pain, and other inefficiencies and downsides associated with thatinvasive test.

As an enhancement to MRI, further aspects of the present disclosureprovide useful diagnostic display to indicate the results in overlaycontext onto the MRI image itself and providing context to thestructures revealed therein.

FIGS. 32A and 32B show two different examples of DDD-MRS diagnosticdisplay results for two different patients in the clinical studyfeatured under Example 1 hereunder. These patients have similar discdegeneration profiles as seen on the MRI images, with dark disc at L5-S1and relatively healthy discs revealed above at L4-L5 and L3-L4 in eachpatient. As also shown in each of these figures, both patients also hadpositive discogram results at L5-S1. However, as also shown in these twocomparison Figures, the patient featured in FIG. 32A had a negativediscogram result (e.g. non-painful diagnosis) at L4-L5, whereas thepatient featured in FIG. 32B had a positive discogram result (e.g.painful diagnosis) at that level—despite having similar discdegeneration profile. As a consequence of both exams, with moderndiscography technique guidelines indicating requirement for a negativecontrol disc before positive levels may be accepted results, thepatients each had another negative discogram done at the L3-L4 (FIG.32A) and L4-L5 (FIG. 32B) levels, respectively, to provide the requirednegative control level. As an awarded recent study has shown discographysignificantly increases disc degeneration & herniations rates, theresult of both of these studies, if followed for directed intervention,would have resulted in treating the positive discogram levels, but notthe negative discogram levels—leaving those untreated levels in place topotentially accelerate in degeneration & toward possible herniations. Asshown in these Figures, the non-invasive DDD-MRS results matched theseinvasive discography results at all disc levels. The DDD-MRS approachprovides the distinct benefit of providing the diagnostic informationrequired, while leaving all discs uncompromised due to the non-invasivenature of the approach.

Turning now to FIGS. 33A-C. The volume excitation achieved using PRESStakes advantage of three orthogonal slices in the form of a double spinecho to select a specific region of interest. In some embodiments, therange of chemical shift frequencies (over 400 Hz for proton at 3.0 T) isnot insignificant relative to the limited band width of most excitationpulses (1000-2000 Hz). The result can be a misregistration of the volumeof interest for chemical shift frequencies not at the transmitterfrequency. Thus, when a PRESS volume is resolved by MRS, the chemicallevels may be not only dependent on tissue level, T1 and T2, but alsodependent on location within the volume of interest. In someembodiments, due to imperfections in the RF pulse, out of volumeexcitation may occur which can present signals from chemicals that arenot in the frequency/location range of interest. Very selectivesaturation (VSS) bands are shown in FIGS. 33A-C.

The following issued US patents are also herein incorporated in theirentirety by reference thereto: U.S. Pat. Nos. 5,617,861; 5,903,149;6,617,169; 6,835,572; 6,836,114; 6,943,033; 7,042,214; 7,319,784.

The following pending US Patent Application Publication is hereinincorporated in its entirety by reference thereto: U52007/0253910.

The following PCT Patent Application Publication is also hereinincorporated in its entirety by reference thereto: WO2009/058915.

Some aspects of the systems and methods described herein canadvantageously be implemented using, for example, computer software,hardware, firmware, or any combination of computer software, hardware,and firmware. Computer software can comprise computer executable codestored in a computer readable medium that, when executed, performs thefunctions described herein. In some embodiments, computer-executablecode is executed by one or more general purpose computer processors. Askilled artisan will appreciate, in light of this disclosure, that anyfeature or function that can be implemented using software to beexecuted on a general purpose computer can also be implemented using adifferent combination of hardware, software, or firmware. For example,such a module can be implemented completely in hardware using acombination of integrated circuits. Alternatively or additionally, sucha feature or function can be implemented completely or partially usingspecialized computers designed to perform the particular functionsdescribed herein rather than by general purpose computers.

A skilled artisan will also appreciate, in light of this disclosure,that multiple distributed computing devices can be substituted for anyone computing device illustrated herein. In such distributedembodiments, the functions of the one computing device are distributed(e.g., over a network) such that some functions are performed on each ofthe distributed computing devices.

While certain embodiments of the disclosure have been described, theseembodiments have been presented by way of example only, and are notintended to limit the scope of the broader aspects of the disclosure.Indeed, the novel methods, systems, and devices described herein may beembodied in a variety of other forms. For example, embodiments of oneillustrated or described DDD-MRS system component may be combined withembodiments of another illustrated or described DDD-MRS systemcomponent. Moreover, the DDD-MRS system components described above, e.g.pulse sequence, signal processor, or diagnostic processor, may beutilized for other purposes. For example, an MRS system (or componentsequence, signal processor, or diagnostic processor useful therewith orthereunder), may be configured and used in manners consistent with oneor more broad aspects of this disclosure for diagnosing other tissueenvironments or conditions than pain within an intervertebral disc. Or,such may be usefully employed for diagnosing pain or other tissueenvironments or conditions in other regions of interest within the body.Such further applications are considered within the broad scope ofdisclosure contemplated hereunder, with or without furthermodifications, omissions, or additions that may be made by one ofordinary skill for a particular purpose. Furthermore, various omissions,substitutions and changes in the form of the methods, systems, anddevices described herein may be made without departing from the spiritof the disclosure. Components and elements may be altered, added,removed, or rearranged. Additionally, processing steps may be altered,added, removed, or reordered. While certain embodiments have beenexplicitly described, other embodiments will also be apparent to thoseof ordinary skill in the art based on this disclosure.

What is claimed is:
 1. A magnetic resonance spectroscopy (MRS)processing system configured to process a repetitive frame MRS spectralacquisition series generated and acquired for a voxel located within anintervertebral disc via an MRS pulse sequence, and acquired at multipleacquisition channels of a multi-coil spine detector assembly, in orderto provide a processed MRS spectrum with at least one chemical regionfrom which spectral data may be extracted and processed to providediagnostic information for a medical condition or chemical environmentin the disc, comprising: an automated MRS signal processor comprising achannel selector, a phase shift corrector, a frequency shift corrector,a frame editor, and a channel combiner, and configured to receive andprocess the MRS spectral acquisition series for the disc and to generateat least in part the processed MRS spectrum for the series; and whereinthe MRS signal processor comprises at least one of (a) at least onecomputer processor and (b) software provided in computer readablenon-transitory storage and that is configured to be run by at least onecomputer processor.
 2. The MRS processing system of claim 1, wherein theMRS signal processor comprises software in computer readablenon-transitory storage and that is configured to be run by at least onecomputer processor.
 3. The MRS processing system of claim 1, wherein thesystem is configured to output the processed MRS spectrum and also anMRI image of a region of the spine that includes the disc.
 4. The MRSprocessing system of claim 1, wherein at least one of the channelselector, phase shift corrector, frequency shift corrector, and frameeditor comprises a water detector configured to detect a feature of awater peak region of an acquired or partially processed spectrumcorresponding with at least one frame.
 5. The system of claim 1, whereinthe MRS signal processor further comprises an apodizer configured toapodize the MRS spectrum.
 6. The system of claim 1, wherein the at leastone said chemical region of the processed MRS spectrum comprises: firstand second regions corresponding respectively with proteoglycan andlactic acid chemicals.
 7. The system of claim 1, wherein the MRS signalprocessor comprises at least one computer processor.
 8. The system ofclaim 2, wherein the MRS signal processor comprises at least onecomputer processor configured to run the software.
 9. The system ofclaim 4, wherein the channel selector is configured to select at leastone channel from the multiple acquisition channels using the detectedfeature of the water peak region corresponding with each of therespective channels.
 10. The system of claim 4, wherein the frequencyshift corrector is configured to recognize and correct a frequency shifterror of at least one frame of the repetitive frame MRS spectralacquisition series by using the detected feature of the water peakregion for the frame.
 11. The system of claim 4, wherein the frameeditor is configured to detect and edit out a first set of excludedframes from the series, and to thereby select and retain a remainingsecond set of retained frames of the series for further processing toprovide the processed MRS spectrum, by comparing the detected feature ofthe water peak region of the respective frames against at least onethreshold frame editing criterion.
 12. The system of claim 11, whereinthe frequency shift corrector is configured to operate on only theretained frames from the frame editor.
 13. The system of claim 12,wherein the frequency shift corrector is configured to calculate andcorrect a frequency shift error of the retained frames, respectively, byusing a second detected feature of the water peak region for therespective retained frames.
 14. A magnetic resonance spectroscopy (MRS)processing method for processing a repetitive frame MRS spectralacquisition series generated and acquired for a voxel located within anintervertebral disc via an MRS pulse sequence, and acquired at multipleacquisition channels of a multi-coil spine detector assembly, and forproviding a processed MRS spectrum for the series with at least onechemical region from which spectral data may be extracted to provideMRS-based diagnostic information for a medical condition or chemicalenvironment in the disc, comprising: receiving the MRS spectralacquisition series from the multiple acquisition channels; signalprocessing the MRS acquisition series, comprising selecting one or morechannels among the channels based upon comparing a measured feature ofacquired data from a channel against at least one threshold channelselection criterion, recognizing and correcting phase shift error amongthe acquired or partially processed spectra corresponding respectivelywith multiple frames within the series for the one or more selectedchannels, recognizing and correcting a frequency shift error among theacquired or partially processed spectra corresponding respectively withmultiple frames within the series of the one or more selected channels,recognizing and editing out a first set of excluded frames and therebyselecting and retaining a remaining second set of retained framesrespectively from the series for the one or more selected channels basedupon at least one threshold frame editing criterion, and combining theretained and phase and frequency shift corrected frames of the one ormore selected channels for a combined average to provide at least inpart the processed MRS spectrum; and wherein the signal processing isperformed by at least one computer processor.
 15. The method of claim14, further comprising outputting the processed MRS spectrum and an MRIimage of a region of the spine that includes the disc.
 16. The method ofclaim 14, further comprising performing at least one of channelselection, phase shift correction, frequency shift correction, and frameediting at least in part by detecting a feature of a water signal alonga region of an acquired or partially processed spectrum correspondingwith at least one of the frames.
 17. The method of claim 14, furthercomprising apodizing an interim partially processed MRS spectrumcorresponding with the series and after frame editing, frequency shiftcorrection, and performing frame averaging between selected frames, tothereby provide at least in part the processed MRS spectrum.
 18. Themethod of claim 14, wherein the at least one region of the processed MRSspectrum comprises: first and second regions corresponding respectivelywith proteoglycan and lactic acid chemicals.
 19. The method of claim 16,comprising performing the channel selection at least in part by thedetection of the water signal feature corresponding with each channel,respectively.
 20. The method of claim 16, comprising performing thefrequency shift correction at least in part by calculating andcorrecting a frequency shift error for at least one frame of the seriesat least in part by using the detection of the water signal feature ofthe frame.
 21. The method of claim 16, comprising performing the frameediting at least in part by performing the detection and editing out ofthe excluded frames, and the selection and retention of the retainedframes, by comparing a value for the detected water signal feature ofthe respective frames against the at least one threshold frame editingcriterion.
 22. The method of claim 21, wherein the frequency shiftcorrection is performed on only the retained frames that are selectedand retained by the frame editing.
 23. The method of claim 22,comprising performing the frequency shift correction at least in part byrecognizing and correcting a frequency shift error for the retainedframes, respectively, at least in part by detecting a second watersignal feature of the respective retained frames.
 24. The method ofclaim 14, comprising performing the signal processing by using the atleast one computer processor to run at least one software programprovided in a computer readable non-transitory storage medium.